Tag Archives: mutation

Updates on the SARS-CoV-2 Outbreak

SHUTTING DOWN THESE UPDATES: The SARS-CoV-2 outbreak has continued to grow and expand its reach. I’m not able to keep up with any semblance of an update at this point, so I will shut this thread down. If things change, and I can contribute, as I’ve done with my lab health plan, I’ll do so in separate posts.

For those wanting summaries of events on a more-or-less daily basis, I recommend the writeups from the University of Minnesota’s Center for Infectious Disease Research and Policy (CIDRAP). For those wanting up-to-the-minute reports of developments, I’ve found  @BNODesk and COVID19 (@V2019N) on Twitter to be reliable.  Others I follow closely on Twitter for synthesis, analysis, and perspective include Helen Branswell (@HelenBranswell), Marc Lipsitch (@mlipsitch), and Carl Zimmer (@carlzimmer). Neil Ferguson (@neil_ferguson) rarely tweets, but provides another expert voice.  Trevor Bedford (@trvrb) provides authoritative analysis of the genomic changes in the virus, which are useful for understanding the dynamics of the global spread. As he’s located in Seattle, he’s also a good person to follow for developments concerning the outbreak in that region.

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To make new information on this viral story easier to find, I’m adding updates with the most recent at the top.

As a reminder, I’m neither an epidemiologist nor a public-health expert, but I study microbial populations from a basic-science perspective. So I have a pretty good sense of what the experts are saying, what is reliable within the stated limits of uncertainty and assumptions, and so on. However, keep in mind that this is a rapidly developing situation, so the “facts” (data and interpretations) may change quickly.

UPDATE 7:55 pm (Feb 29): So much news today that I feel unable to provide even a short summary, including an apparent cluster of cases involving patients and staff at a nursing home in the state of Washington. Fortunately, others have provided summaries of today’s coronavirus news, including this clear and authorative writeup from CIDRAP. I would encourage individuals, families, businesses, local governments, and other groups to begin thoughtfully preparing for the possibility of infections in your own communities, if you haven’t already done so. I’ve posted the planning document for my own lab group.

UPDATE 11:00 & 11:30 pm (Feb 28): Second, third, and now fourth cases of infections in the US without travel history or known conacts with infected people–the second such case in California, and the first each in Oregon and Washington. The Washington case is a teenager, and the infected person in Oregon works at a school, adding yet additional worrisome elements. New cases and fatalities continue to rise dramatically in South Korea, Italy, and Iran. Several dozen other countries now also have cases. Markets in turmoil, leadership unfocused and/or in denial here in the US and some other places as well.  Here’s a 25-minute podcast interview with Harvard infectious-disease epidemiologist Marc Lipsitch about why he thinks it’s likely now that perhaps half of all adults will be infected at some point (but not all at the same time), and how to think about your own preparations. It’s well worth listening to the whole interview, whether you’re a scientist or not. (There are a few slightly technical bits, but none that go on for long–the interview stays focused on the big picture.) Lipsitch is very clear and soft spoken, despite the troubling implications of this new disease.

UPDATE 10:00 pm (Feb 27):  The big news reported here in the US is an infection in northern California with no international travel nor, apprently, known contacts with anyone infected, suggesting a case of community transmission. Of interest, and possible concern, that viral isolate’s genome differs by only a single mutation from another California isolate. Could that have come from a repatriated and quarantined individual?  If that proves to be the case–and I repeat, if-– then it suggests there was some “leak” in the quarantine.  Meanwhile, individuals involved in those repatriations may not have received appropriate gear and/or instructions to protect themselvesCalifornia health officials are monitoring thousands of individuals, but lack necessary kits to test for infections. Elsewhere in the world, Japanese schools are told to close for the next month. French and German authorities announce that epidemics are underway in those countries, while new cases continue to mount in South Korea, Italy, and Iran.

UPDATE 9:30 pm (Feb 26):  South Korea reported another 334 cases just today, bringing the total there to almost 1600 from just 31 a week ago.  And Italy reported 52 new cases, bringing its total to 128.  Here’s an image of an empty scientific meeting in Trieste, Italy — the meeting was cancelled, but the lectures were made available online. How many more cancellations of social, scientific, and business gatherings might we see in the weeks and months ahead?  Meanwhile, several countries reported their first SARS-CoV-2 cases today, including Brazil, following an individual’s travel from Italy … and with Carnival getting underway, that’s a potentially worrisome development there. Germany’s Ministry of Health has now found 18 cases, and officials warn of an epidemic potentially starting there.  A new case in California, someone with no relevant travel history, is a concern in the US because it suggests community spread.

UPDATE 9:00 pm (Feb 25):  South Korea cases up to 1,146 cases, up from just 31 less than a week ago. In Iran, who knows many cases of COVID-19, but the deputy health minister has it.

UPDATE 8:00 pm (Feb 24):  Five countries in Middle East report first cases, while Iran’s cases are up to 61, with 12 fatalities.  Italy’s cases up to 227, including 6 deaths.

UPDATE 7:10 am (Feb 24): Over 60 new cases reported today in Italy, with total now over 200 and 5 deaths to date. Official report from Iran states 12 deaths, but a lawmaker there says the actual number is much higher. South Korea total cases now reported as 833, with 7 deaths.

UPDATE 9:00 pm (Feb 23): Cases in South Korea continue to grow, now at 763 from just 31 several days ago.  Given the delay between infections and deaths (a few weeks, for those unfortunate minority of cases), the deaths in Italy indicate a larger outbreak that has grew undetected. Same for Iran, but we’re less likely to get reliable data from there.

EXPERT PERSPECTIVE 11:55 pm (Feb 22):  What countermeasures can we take to reduce the harm and disruption caused by this outbreak-turned-pandemic?  Read this excellent thread from epidemiologist Marc Lipsitch about short-term and long-term strategies for dealing with the expanding SARS-CoV-2 outbreak and the COVID-19 disease that it causes. Full of ideas, advice, evidence, and concern for our individual and collective well-being.

UPDATES 9:30 pm (Feb 22): Where to start? How about South Korea, from 31 cases a few days ago to more than 500 (with 4 deaths) today. Or maybe Japan, where a quarantine officer at an airport has contracted the infection.  Then there’s Italy, with dozens of new cases today and many of those seriously ill, 11 towns in “lockdown” mode, and no clear understanding of how the outbreak began in that country.  And what’s up with Iran, which has an outbreak of unknown scope, and which has apparently exported cases to other countries including Canada and Iraq, which suggests the outbreak in Iran is large.  And the US? It’s hard to tell.  Have we been diligent and successful in limiting, identifying, and isolating potential infected persons?  Or have we been slow and too limited in testing?

PERSPECTIVE 9:10 pm (Feb 22): It pains me to write this, but I think there can be no doubt now that we are in the midst of pandemic caused by the SARS-CoV-2 coronavirus. No, it does not mean the end of the world, nor pervasive death, nor anything like that. But it does mean this infection has spread to, and continues to spread in, multiple countries on multiple continents. And with its spread will come substantial illness in some or many communities, some deaths, pressure on health-care systems, personal inconvenience, economic disruption, and discomforting uncertainty.  Stay strong, everyone. Respect and help your neighbors, while also practicing basic hygiene like handwashing, tracking the news in your own country and community, and so on.

STATUS 9:00 pm (Feb 22): I’ve been preoccupied today with an analysis of mutations in the SARS-CoV-2 genome. It’s undoubtedly a small side story, at best, but it interests me scientifically.  Also, I am “splitting” this web page into two parts with this page providing occasional updates to scope of this–yes, I will now call it what it has become–pandemic. I’ve added a separate post that provides occasional links to, and discussion of, Expert Analyses of the SARS-CoV-2 Coronavirus Outbreak.

UPDATE 10:10 am (Feb 22):  A large jump in SARS-CoV-2 infections reported in Italy, up to 54 from just 21 yesterday. Also, a second death was reported there.

UPDATE 10:15 pm (Feb 21): Another big jump in cases in South Korea.  The 142 new cases bring the total to 346 — it was just 31 three days ago.

UPDATE 11:30 am (Feb 21):  A new cluster of six youngish (~40 years old) cases, this one in Italy, and most of them are in serious condition. This cluster is possibly linked to another cluster elsewhere in Italy. About 250 contacts have been placed in isolation and will be tested for the SARS-CoV-2 coronavirus.

UPDATE 9:40 am (Feb 21):  Two prisons in China now reporting hundreds of cases.

UPDATE 9:30 am (Feb 21): Iran is reporting multiple cases in several cities: “The spread of the coronavirus started in Qom and with attention to people’s travels has now reached several cities in the country, including Tehran, Babol, Arak, Isfahan, Rasht, and other cities. And it is possible that it exists in all cities in Iran,” according to an official of Iran’s Health Ministry. Meanwhile, Lebanon has reported its first case–a traveler from Iran.

UPDATE 8:50 am (Feb 21): Now more than 200 cases in South Korea. Helen Branswell has posted a graph of the sudden rise of COVID-19 in that country.

UPDATE 10:50 pm (Feb 20):  Yet more cases reported from South Korea. The total is now 156. Many, but not all of them, are part of a large cluster associated with a church group.

UPDATE 9:45 am (Feb 20): Yikes again, Helen Branswell of statnews just reported that South Korea has had another jump to 104 in confirmed cases, up from just 31 two days ago. Not clear to me whether these are symtomatic cases of the disease COVID-19, or confirmed infections with the SARS-CoV-2 coronavirus.  And if I read this report correctly, there are another 1,860 suspected cases awaiting test results, although many previous suspected cases have returned negative results.

UPDATE 9:35 pm (Feb 19):  Yikes, this new report has South Korea’s cases up to 82, from just 51 yesterday and 31 before that.

UPDATE 6:20 pm (Feb 19): The latest concersn are jumps in the number of reported cases in some countries, including ones that hadn’t previously reported SARS-CoV-2 infections. South Korea reported a big jump from 31 to 51 cases, including a cluster of 16 cases in one city. This demonstrated the potential, at least for outbreaks to take hold outside of China. Meanwhile, Iran has few travelers from China, and was not known to have any cases. But two cases were reported today in Iran, and both patients have died. The Iranian health ministry is trying understand how those individuals became infected. And in Japan, the number of cases has more than doubled in a week, with 74 documented cases. And that doesn’t include the 600+ cases on the cruiseship Diamond Princess, now docked near Tokyo. Many of the other passengers are now being released from quarantine. But given the ever-increasing number of positive tests that were discovered, have new asymptomatic and/or presymptomatic infections been released into Japan and beyond? All of these reports are concerning not because they represent a large number of cases–the number of cases in all other countries combined remains far smaller than the number in Wuhan alone. Rather, the concern is that these represent and/or may seed new outbreaks that will be increasingly hard to trace and contain, as in the possible scenerios discussed by Trevor Bedford (see Expert Perspective posted at 4:30 pm on Feb 8), Richard Neher (see Model of Global Spread posted at 8:45 pm on Feb 9), Marc Lipsitch (see Expert Perspective posted at 1:15 pm on Feb 14), Neil Ferguson (see Expert Perspective posted at 8:00 pm on Feb 16), and other experts.

UPDATE 11:45 am (Feb 18):  Today’s report from Japan brings another 88 cases of SARS-CoV-2 infections on the quarantined Diamond Princess cruise ship. That brings the total number of infections detected so far to 542.

EXPERT ANALYSIS 9:50 am (Feb 18):  Adam Kucharski explains why estimating the case fataility rate for COVID-19 is complicated, and why the rate can appear to increase when the outbreak is slowing down.

EXCELLENT RESOURCE 7:30 pm (Feb 17):  The Center for Infectious Disease Research And Policy (CIDRAP) at the University of Minnesota has a superb (almost) daily summary of news about COV-19 and the SARS-CoV-2 virus.  Here’s the summary for today, and here’s the webpage linking to all the summaries — bookmark it!  (h/t @mlipsitch)

UPDATE 4:30 pm (Feb 17): Some readers may recall debate (and confusion) about whether the most closely related viral sequences to the SARS-CoV-2 outbreak had come from pangolins (scaly anteaters) instead of from a bat. [See the update titled “Waiting for more info but …” posted on Feb 7.]  The genome sequences of several SARS-like viruses sampled from pangolins have been shared by scientists from Beijing and Hong Kong. Trevor Bedford has included these sequences in his latest phylogenetic analysis to assess relatedness. As Bedford explains, “these pangolin viruses are closely related to the COVID19 epidemic, but [they are an] outgroup relative to bat/Yunnan/RaTG13/2013.” In other words, the isolate sampled from a bat in Yunnan in 2013 remains the closest relative seen thus far to the SARS-CoV-2 that caused the outbreak in Wuhan. Bedford points out, though, that “additional sampling may reveal a direct intermediate” from bats, pangolins, or some other animal. (Bedford posted another thread that elaborates on the difference between this latest work and some of the confusing earlier reports, for which no data has been made public to date.)

UPDATE 12:10 pm (Feb 17):  CDC has updated their test results for SARS-CoV-2 in the US. The latest report shows 15 postive and 392 negative tests, with 60 other cases pending. These data mean that the most recent 45 tests (since the update on Feb 14) have all been negative. There are a number of people with confirmed infections who have been evacuated from the Diamond Princess cruise ship, who are returning to quarantine in the US. It’s unclear whether they will be counted in futue CDC reports.

UPDATE 11:00 am (Feb 17): As much as it’s been a disaster for the affected individuals, the continuing outbreak aboard the Diamond Princess is providing valuable information on the distribution of the severity of outcomes for infected individuals. About a quarter of those tested so far have been infected.  Of those whose tests indicate they are infected, about 40% are asymptomatic (or, perhaps, presymptomatic). Of the 60% with symptoms, about 7.5% (= 19/254) are in critical condition. Of course, we need to keep in mind the demographics of the passengers, most of whom are older than the overall population. The large crew population, which is generally younger, should provide a valuable contrast for epidemiologists. (h/t @drkuehnert)

MODEL WITH SEASONAL VARIATION IN TRANSMISSION 10:30 am (Feb 17):  Richard Neher, Emma Hodcroft, and co-authors have posted a paper (not yet reviewed by other experts) where they analyze the possible effects of seasonality on the extent of the SARS-CoV-2 outbreak. Their model assumes that the virus, owing to its transmissibility and the global mobility of people, will eventually become established globally. (That’s not certain at this time, but many experts think it is likely.) They begin by noting that four other coronaviruses that circulate in the human population (typically causing symptoms similar to the common cold) are more prevalent in the winter and early spring. The good news from their model is that this seasonal variation in transmission should slow the spread of the new coronavirus in the coming months. The bad news, though, is that SARS-CoV-2 infections are likely to reach a peak next winter (2020/2021). At least that provides more time for health-care systems to prepare. They also emphasize that health officials and others should not assume the virus is under control based on diminishing case counts, because seasonality (along with quarantines and other social-distancing efforts) may give a false impression that the virus has been brought under control. Hodcroft has a nicely illustrated and explained Twiiter thread that summarizes this work. A few hours later, Neher posted another excellent thread on this work.

UPDATE 10:00 am (Feb 17):  Japan officials reported that another 99 cases of SARS-CoV-2 infections on the Diamond Princess cruise ship, docked near Toyko. That brings the total number of infections to 454, out of about 3700 passengers and crew in total.  Yesterday, more than 300 Americans who had been on the ship were flown from Japan and will be further quarantined in the US.  For those left onboard the ship, their quarantine was supposed to end this Wednesday … but given the ever-growing number of infections, that seems unlikely.

EXPERT PERSPECTIVE 8:00 pm (Feb 16): Neil Ferguson is an epidemiologist who models the dynamics of infectious diseases. In a technical, yet sobering, interview he works through estimates of various quantities relevant to the SARS-CoV-2 outbreak. He takes great care to acknowledge the uncertainties around his estimates. Here’s my effort to summarize what I understand him to say.

  1. The large number of cases on the Diamond Princess cruise ship shows how easily the virus spreads.
  2. The number of cases in Wuhan (city) and Hubei (province) appears to be plateauing, as predicted given the stringent quarantine imposed weeks ago.
  3. It’s hard to know what’s happening elsewhere in China because they only test people with travel history to Wuhan and Hubei, which would miss community transmissions and thus under-estimate the extent of the outbreak elsewhere.
  4. There are anecdotal reports of surges in pneumonia cases in other cities in China, consistent with under-testing and under-reporting of the new coronavirus.
  5. Regarding the severity of this disease, it’s difficult to say in part because different surveillance methods pick up different categories of severity.
  6. In China, only the most severe cases are routinely tested for the virus. Ferguson’s team estimates that about 18% of the severe cases in the Wuhan epicenter may die.
  7. That does not mean, however, that 18% of the people infected die because many have mild or even no symptoms, and they are not tested. Ferguson’s team estimates that only about 5% of infected people are actually tested in Wuhan. So combining this fraction with the severe cases, one would estimate an overall mortality rate across all infections (mild and severe) of roughly 1%.
  8. Another comparison group includes the ~300 cases of international travelers, where there have been 2 deaths (as of the time of this inetrview). However, there is a delay of ~3 weeks between diagnosis and death in the severe cases, and so that fraction needs to be adjusted to account for this delay. When accounting for this delay, Ferguson estimates that the mortality rate will eventually prove to be between 2% and 5% in this group. Once again, however, these cases are focused on travelers who already showed observable signs of illness when they entered a country, so this rate will also be higher than for other infections.
  9. To adjust for this bias in detection as a function of severity, one needs to estimate the fraction of all travelers from the affected areas who are infected. To estimate this infection prevalence, Ferguson uses data obtained from the evacuation flights, where travelers who returned to their home countries were systematically quarantined and tested for the coronavirus, whether or not they showed symptoms. From these data, Ferguson estimates there were 3 to 4 times more infections than discovered when screening travelers. This means two things. First, the mortality rate estimated from travelers who show symptoms is once again too high by several fold,  If all infections were taken into account, the overall death rate is something on the order of 1%. Second, it means that many countries probably have SARS-Cov-2 transmissions occurring undetected in some communities.
  10. Given all of the statistical noise in the data, Ferguson says that the uncertainty around these estimates of 1% mortality is about 4-fold in each direction. So bottom line, he thinks the true mortality rate lies between about 0.25% (1 in 400) and 4% (1 in 25).
  11. The lower value would be similar to the pandemic influenza years of 1957 and 1968, while the high end would be more comparable to the 1918 pandemic.
  12. The potential scope of the pandemic in terms of how many people will be infected is also difficult to predict. Going from past experience with influenza pandemics, Ferguson suggests that roughly half of the population might be infected in the first year, when one includes both those people who become ill and those with mild or no symptoms.
  13. Despite these uncertainties, Ferguson explains that such numbers are valuable for countries and their health-care systems to formulate appropriate plans to deal with this “serious threat.” 

NEW INFO 7:00 pm, updated at 10:05 pm based on corrected info from Dr. Gottlieb (Feb 16):  Scott Gottlieb, an MD and former commissioner of the FDA, makes an interesting comparison between the number of cases of COVID-19 in the US versus Japan and Singapore. Japan has 4 times as many known cases as the US, despite having only twice as many travelers from China. (Note: Gottlieb is not including those on the cruiseship quarantined near Tokyo.) Singapore has 5 times as many cases as the US, with about the same number of Chinese visitors. Both Japan and Singpaore have some community transmission involving unknown contacts. Taken at face value, these comparisons “might suggest there are undiagnosed cases in U.S.

UPDATE 3:45 pm (Feb 15):  The Diamond Princess is not the only cruiseship with a troubling story. The Westerdam was turned away from several Asian ports over concerns of the new SARS-CoV-2 coronavirus. However, it was allowed to dock in a Cambodian port yesterday, and 2257 passengers and crew were allowed to disembark after some health screening. A group of 145 of the ship’s passengers then flew to Malaysia — and one, an elderly American woman, had symptoms when she landed. She has reportedly tested positive for the virus, while her husband did not.

NEW INFO 3:15 pm (Feb 15):  Epidemiologist Michael Mina summarizes an important study from a team of medical scientists in Wuhan. (The linked paper is a preprint, and it has not been fully vetted by other scientists. However, it passed muster with an expert in the field, which suggests to me that it provides valuable information.)  Over 8000 people identified as contacts of people with COVID-19 were tested for the SARS-CoV-2 virus that causes that disease. More than one-third of the contacts tested positive for the viral infection, reinforcing the contagiousness of this disease. Fortunately, however, most of the infected contacts had only mild symptoms and were not sick enough to require medical care — at least not when they were tested. Presumably, the contacts have been quarantined and their health will be tracked.

INTERESTING READ 2:05 pm (Feb 15):  An interesting news story from UC-Berkeley about why bats seem to carry many viruses that cause problems when humans acquire those infections.

NEW INFO 1:00 pm (Feb 15):  The US readies coronavirus quarantine facilities at 15 military bases around the country

MONEY LAUNDERING 12:40 pm (Feb 15): “Money from key virus-hit areas [in China] will be sanitized with ultraviolet rays or heated and locked up for at least 14 days, before it is distributed again,” according to Fan Yifei, deputy governor of the People’s Bank of China.

UPDATE 11:00 am (Feb 15):  First COVID-19 death reported in Europe, as an 80-year-old Chinese tourist dies in France of the new coronavirus after 3 weeks in hospital.

UPDATE 9:35 am (Feb 15):  A second suspected case of COVID-19 reported in Africa, this one in eSwatini (Swaziland), near South Africa. Yesterday a case was confirmed in Egypt. Both cases followed international travel.

UPDATE 9:20 am (Feb 15):  From Helen Branswell, Singpore now has 72 confirmed cases of COVID-19. Despite diligent epidemiological tracking, however, it remains unclear how 8 cases became infected with the SARS-CoV-2 that causes the disease COVID-19.

UPDATE 8:30 am (Feb 15): Another 67 cases of SARS-CoV-2 infections have tested positive onboard the Diamond Princess cruiseship quarantined near Tokyo. That brings the total to 285. Also, the CDC announced that it will evacuate all US healthy citizens from the ship and bring them to military bases in the US, where they will undergo further quarantine. Those who have already tested postive and/or who have symptoms may have to remain quarantined in Japan for a while longer.

MODEL WITH VARIABLE TRANSMISSION 10:45 pm (Feb 14): Kyra Grantz, Jessica Metcalf, and Justin Lessler tackle an apparent dilemma in the epidemiological data on the spread of the coronavirus SARS-CoV-2. On the one hand, the value of R0 appears to be greater than 2 based on data from China, meaning that each infected person, on average, infects 2 or more people. On the other hand, most infected travelers do not seem to have set off significant transmission chains outside of China (although there certainly have been some secondary infections). How can these patterns be reconciled? In short, the resolution may lie in the variability between infected persons–or the settings when they are most infectious–in their propensity to infect others. So, for example, if most infected people start to feel a bit sick and stay home, but a few still feel well enough to go to a conference, then the average number of transmissions over all cases might be 2, but the variation in the number of transmissions could be quite high. In that case, most introductions of an infected traveler into a new community may lead nowhere (and appear inconsistent with a high rate of spread), but the occasional introduction could lead to a much larger (and potentially hard to contain) outbreak. In this scenario, it becomes harder to control the spread of an epidemic unless one can systematically identify the situations where such “super-spreading” events tend to occur; if one can identify and prevent those situations, however, then control of the outbreak may be more feasible. Lessler clearly explains and illustrates the ideas in a Twitter thread

NEW INFO 8:30 pm (Feb 14):  First confirmed case of COVID-19 (name of the syndrome caused by the new coronavirus) in Africa. The case was a traveler diagnosed in Egypt.

NEW INFO 8:20 pm (Feb 14):  The CDC will begin testing people with flu-like symptoms for the SARS-CoV-2 in five cities (Chicago, Los Angeles, New York, San Francisco, and Seattle), according to Scott Gottlieb, former FDA commissioner. This information won’t halt any outbreak that gets underway, but it will indicate when and where the virus gains a footfold in the US, and at what prevalence among people with flu-like illness. 

UPDATE 3:15 pm (Feb 14):  The latest update from CDC on testing for the coronavirus SARS-CoV-2 in the US is now showing 15 postive tests and 347 negative tests, with 81 cases pending. That’s 3 new positive cases and 29 negative results since the last update that I reported on Feb 10. 

UPDATE 3:00 pm (Feb 14): Epidemiologist Marc Lipsitch and colleagues estimate that fewer than half of COVID-19 cases are being detected in travelers, based on data from Singapore. This implies that there are more cases than known. It also implies that the average case severity is lower, because the less severe cases are more likely to escape detection. That said, however, those less severe cases may transmit the coronavirus, leading to more infections–including severe cases–in the long run. 

IN-DEPTH ANALYSES 1:30 pm (Feb 14): Computational biologist Joshua Weitz shared with me the link to a special event featuring 3 short talks on the SARS-CoV-2 outbreak that was organized this past Monday by the Center for Microbial Dynamics and Infection at Georgia Tech. The first speaker was Trevor Bedford, who leads the Nextstrain project that uses changes over time in microbial genome sequences, including the SARS-CoV-2 coronavirus, to understand the origin, transmission, and evolution of various pathogens. The second speaker was Weitz, who spoke about how experts use data to estimate the strength, speed, and final size of disease outbreaks in general, and the ongoing coronavirus outbreak in particular. He provided a separate link to his very clear slides. Importantly, Weitz explains some some of the uncertainties associated with these estimates, and some implications of these uncertainties for understanding the future of this outbreak. The third speaker was Phil Santangelo, who spoke on potential strategies used in antiviral drug design.

EXPERT PERSPECTIVE 1:15 pm (Feb 14): Infectious-disease epidemiologist Marc Lipsitch is quoted in the Wall Street Journal as saying It is likely we’ll see a global pandemic … If a pandemic happens, 40% to 70% of people world-wide are likely to be infected in the coming year.” In an informative Twitter thread, Lipsitch elaborates on why he thinks this pandemic scenario is likely, and what factors might prevent a pandemic from unfolding (including control measures, especially in countries with strong healthcare systems). He closes by saying that “Predictions can be wrong and I very much hope this is, but better to be prepared.”

NEW INFO 12:45 pm (Feb 14): In another worrisome development, Japan is experiencing a “stealth outbreak” with several cases of the coronavirus infection in people without any travel history to China, and some without any known links to others who have had this infection. These findings led infectious-disease modeler Richard Neher to tweet that “Reports like this make me doubt that containment of SARSCoV2 is likely.” You can read more about Neher’s concerns in the “Model of Global Spread” post below (8:45 pm on Feb 9). 

NOT SHIPSHAPE 11:40 pm (Feb 13): The situation on the cruiseship Diamond Princess continues to worsen. The passengers and crew are quarantined onboard the ship, which is docked near Tokyo. A passenger who was onboard the ship from January 20-25 was subsequently found to be infected in Hong Kong. Now a total of 218 passengers and crew have been found to be infected, with the totals increasing by over 40 since the previous report just two days ago. Given a difficult situation that seems to have been handled poorly, one can only hope that at least some valuable epidemiological evidence will come from the cases. It would be nice, for example, to have multiple viral genomes sequenced from each of the infected individuals, along with information about the people’s onboard contacts, dining, proximity of cabins, ventilation, etc. Similarly, given the relatively large (but still manageable) number of cases, this outbreak might provide better information on the distribution of severity in a country, Japan, with a strong (and not overwhelmed) medical system.

AN UNKNOWN 11:30 pm (Feb 13): Despite speculation that the SARS-CoV-2 outbreak will be limited by seasonality, that remains unknown. One of many important unknowns at present.  (h/t @mlipsitch)

STATUS 11:00 pm (Feb 13): Sorry for the lack of any updates over the previous two days. My work-related travel made it impossible. I did manage to retweet a few stories when I had breaks, but I wasn’t really able to synthesize things in my own mind.  This was mostly a reflection of my travel, but perhaps it’s also an indication that we have entered a period of considerable uncertainty when it comes to this viral epidemic. Before the quarantines in Wuhan and other Chinese cities, we had frequent numbers that allowed estimates of R0 and other quantities. Undoubtedly the quarantines helped reduce the rate of spread, but they also made it harder to interpret the more recent data.  Also, there are uncertainities associated with testing capacity, false negatives (failure to detect the virus in some infected people), and even changing criteria for diagnosing COVID-19. Some combination of these factors presumably explains the huge jump in cases reported yesterday–almost 15,000 newly confirmed cases in Hubei province, or about 10 times as many as the previous day. There also remains a lot of uncertainty about the distribution of the severity of the infection. That’s in part a reflection of the fact that the distribution of the severeity is very broad, with many infections producing mild symptoms (or none at all) and others requiring prolonged hospitalization, often under intensive care.

NEW NAME 2 pm (Feb 11): The coronavirus formerly known as nCoV2019 has been renamed COVID-19.  Correction: COVID-19 is the new name of the disease, not the virus itself. AND the virus is now named SARS-CoV-2 (for severe acute respiratory syndrome coronavirus 2).

UPDATE 11:10 pm (Feb 10): Several nCoV2019 genomes have been sequenced from Japan and Vietnam, and the data added to the @nextstrain phylogenetic tree. This tree shows the ancestry of the virus, based on the mutations that accumulate in different lineages. The new viral sequences from Japan and Vietnam fall in the large, growing cluster that includes most of the international travel-related cases.

UPDATE 10:35 pm (Feb 10):  Latest update from the CDC on testing for nCoV2019 in “people under investigation” (PUI) in the United States. They are currently reporting only 12 postive tests (same as last report), and now with 318 negative tests. So all 93 tests since the last update have been negative. However, there is a report tonight of another positive test, this one in San Diego for a person on one of the quarantined evacuation flights out of China, This person first tested negative, but was retested and found to have the nCoV2019 infection.

NEW INFO 8:30 am (Feb 10):  Two nCoV2019 viral genomes were isolated by the Chinese CDC from environmental samples at the Huanan Seafood Market in Wuhan, and they have been sequenced. The @nextstrain team analyzed the sequences, and they cluster with other early sequences isolated from infected people. This result strongly bolsters the supposition that this market was the source of the initial outbreak. Despite “seafood” in the market’s name, many other animals were sold there. Trevor Bedford suggests that the high level of viral contamination detected there might have been associated with butchering an infected animal, which seems quite plausible. In any case, the virus has subsequently been spreading from person to person.

MODEL OF GLOBAL SPREAD 8:45 pm (Feb 9): Biophysicist Richard Neher updates his graphical presentation of the increase in nCoV2019 cases in Hubei and elsewhere in China. The good news is that the rate of increase is declining. That’s expected given the extreme quarantine measures taken in Wuhan and other cities. However, it’s also clear that many smaller outbreaks have been seeded elsewhere in China and other countries. Some of these outbreaks will be contained with expertise and diligence, but others will likely escape notice until they become too large to contain. With travel, these outbreaks can seed new outbreaks, and so on, as discussed and illustrated by Trevor Bedford. (See the Expert Perspective posted below at 4:30 pm on Feb 8.) Neher has run simulations to get a handle on this scenario, using his educated guesses for the relevant parameters. In brief, he assumes there are many such outbreaks already underway, but running 2 or 3 months behind the Wuhan outbreak. With increased awareness among the public and health-care workers, many of these outbreaks will grow more slowly than Wuhan did and be contained. And even those that grow large will, like Wuhan, slow down once they become very large due to quarantines and other social distancing.  Nevertheless, Neher finds it’s quite possible to envision total global cases in several months that dwarf those seen in Hubei by 100-fold, even while it looks as though (as it does now) that the rate of increase is declining. I’ve posted a screen shot of this scenario below.

Nerer global scenario 09-Feb-2020

CONCERN NOTED 4:00 pm (Feb 9): Responding to news of several unlinked cases of nCoV2019 in Singapore, infectious-disease epidemiologist Mark Lipsitch notes that they “are expert at contact tracing [but this situation] increases level of concern that similar transmission may be occurring under the radar elsewhere. And deflates the notion that tropics not vulnerable.”

THINKING OUT LOUD 10:30 am (Feb 9): Following my comment on Twitter that draws attention to the large, globally emerging clade in the nCoV2019 sequence data (as I also explained in the Update just below), Dr. Emma Hodcroft commented that “it might be worth exploring a different root for the tree, given this cluster. Certainly notable!” That led me to recall that epidemiological data now indicates that, despite most of the early cases being associated with the Wuhan Seafood Market, there were a few even earlier cases that did not have any connections to that market. Therefore, the earliest sequenced viruses (which include several identical Wuhan islates and offshoots from those) likely derive from the market-associated outbreak. This large, emerging, globally spreading cluster of nCoV2019 sequences may well derive from the incipient outbreak that pre-dated the market-associated outbreak. Updated (see the New Info at 8:30 am Feb 10): With the new finding that environmentally isolated viral genome sequences from the market in Wuhan closely match many of the early cases, it now seems likely that the market was indeed the source of the oubreak. That said, I think the large, globally emerging clade discussed here bears watching and in-depth epidemiological investigation.

UPDATE 9:45 am (Feb 9): The @nextstrain team has just added several more genome sequences from nCoV2019 isolates to their analysis pipeline. There had been a period without new sequence data from China, so the only new data were coming from overseas cases. This update adds several more recent (but still January) sequences from China and Taiwan. What strikes me is the large cluster that emerges down at the bottom of the image (copied below), with cases now in mainland China, Taiwan, Korea, the US, Australia, England, and Belgium. And those in China include 1 sequence from Wuhan (WH04/20202) at base of this clade to two of the sequences just added (from Yunnan and Sichaun).

nCoV2019 nextstrain 09-Feb-2020

EXPERT PERSPECTIVE 4:30 pm (Feb 8): A short, and excellent, thread from infectious disease epidemiologist Trevor Bedford explaining why the next several months are so critical for the potential for global spread of nCoV2019. In essence, the Wuhan outbreak seeded new outbreaks in China and elsewhere. We now know that the Wuhan outbreak was not contained until it became huge. (See, for example, my simple estimate from 27 January that there were already on the order of 100,000 infected people in Wuhan). Not all of these infections led to “cases” in the sense of hospitalized patients or even those that saw a doctor. That’s because most infected people have only mild symptoms, but it appears that these mild infections can still contribute to the virus’s spread. (See updates below from Feb 4, regarding such cases in Germany and Hong Kong. There are probably many more such cases.) Importantly, all of the newly seeded outbreaks have to be much better contained to keep things in control. Bedford nicely shows this basic idea in his hand-drawn picture, which I’ve reproduced here.Seeded outbreaks from Bedford

UPDATE 10:20 pm (Feb 7):  Latest update from the CDC on testing for nCoV2019 in “people under investigation” (PUI) in the United States. Currently reporting 12 postive tests, with 225 negative tests, and 100 cases pending. So ~5% of cumulative tests have proven positive to date, similar to CDC’s recent reports. As noted two days ago, CDC will also now allow states to perform these tests. No states have reported test results to date, to my knowledge. Will states report test results directly, or via these CDC summaries?

CONCERN NOTED 12:45 pm (Feb 7):  Epidemiologist Maia Majumder expresses concern about nCoV2019 cases onboard cruise ships, including one quarantined off Japan, which now has 41 new cases (making a total of 61).  She points out that “cruise ships are notorious for infectious disease activity. A confined space with shared water, sanitation, and hygiene infrastructure; predominant activity in community spaces; and a plethora of buffets … will do that.”

NEW INFO 12:30 pnm (Feb 7): JAMA Network report on “Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus–Infected Pneumonia in Wuhan.”   Summary of findings reads as follows:  “In this single-center case series involving 138 patients with NCIP, 26% of patients required admission to the intensive care unit and 4.3% died. Presumed human-to-human hospital-associated transmission of 2019-nCoV was suspected in 41% of patients.”

NEW INFO 11:10 am (Feb 7): Helen Branswell reports that Maria Van Kerkhove, an epidemiologist with the World Health Organization, has analyzed data from 17,000 cases of nCoV2019 infections and found that “82% of cases are mild, 15% are severe and 3% are critical.” (Link to the verbal report here.) Numbers are presumably still based only on the cases that make it into the Chinese medical reporting system–without any serological testing of the population at large. So hopefully the percentages of severe and critical infections (versus cases) will turn out to be much lower. On the other hand, more undiagnosed cases mean more opportunities for long transmission chains in the community. The quarantines in hard-hit Chinese cities, and social distancing elsewhere, should reduce the number of such chains. But are they enough to halt the spread?

NEW INFO 11:00 am (Feb 7):  STAT news reporting that US hospitals are preparing for a possible spike in coronavirus cases. “Much of that work revolves around hospitals planning for what [Dr. Paul] Biddinger [medical director of emergency preparedness at Massachusetts General Hospital] called the ‘four S’s’ of a surge in patients amid an outbreak: supplies, staff, space, and the system that governs all of them.”

WAITING FOR MORE INFO BUT … 10:45 to 11:45 am (Feb 7): Reports circulating on Twitter that a  coronavirus isolated from a pangolin is an even closer genomic match to the Wuhan virus nCoV2019 than the closely related isolate from a bat. I await expert analysis and confirmation (or refutation) from Trevor Bedford and others. Pangolins are also called scaly anteaters. They are sometimes used in traditional Chinese medicine and their meat is considered a delicacy by someExpert Richard Neher weighs in. He says if the claim is based on the same pangolin-derived virus isolate that was discussed previously, then its genome sequence is certainly more distant to the outbreak isolates than one from a bat. However, infectious disease epidemiologist Tara Smith counters that the previously discussed virus isolate from a pangolin came  from another country, and there should be forthcoming new genome sequences from coronaviral isolates from pangolins recently obtained in China. In other words, this issue of the animal source still appears unsettled. Nature also has a news story about this, but nothing yet published in terms of data.

NEW INFO 11:00 pm (Feb 6):  Using expectations based on travel patterns from Wuhan to other countries, Marc Lipsitch and colleagues from Harvard’s School of Public Health suggest that Indonesia, Thailand, and perhaps Cambodia are “missing” nCoV2019 cases. This pattern means that the virus might be escaping notice, allowing more opportunities for it to gain a foothold in those countries.

EXPERT PERSPECTIVE 8:35 pm (Feb 6): Tom Inglesby is the Director of the Johns Hopkins University Center for Health Security. He has a must-read twitter thread calling on the US and international political, medical, and public-health communities to undertake coordinated action to prepare for a possible pandemic. Some of the points Dr. Inglesby makes (with my emphasis in bold):

  • Continued efforts are being made toward containing and ending the nCoV outbreak before it leads to widespread community transmission in countries around the world. However what we know about this virus /epidemic suggests this goal is likely not attainable”
  • “… extent of nCoV in China w/daily rise in numbers; high connectedness of China to rest of world; high r(0) of virus; spread of cases before containment started; cases in other countries that seem only explainable by community transmission – all suggest nCoV not containable.”
  • “Domestically — Is there community transmission in US? Given the thousands of passengers arriving daily from China in last 2 months prior to the recent travel restrictions, and given how transmissible nCov is seems possible, if not likely, there is already US transmission.”
  • Need plan of action for ramping up preparedness of US hospitals to care for high numbers of ARDS [acute respiratory distress syndrome] patients. Training & protecting HCWs [health care workers]. Assessment and management of PPE [personal protective equipment] supply. Understand vent supply in private sector and SNS [social netork services]. Screening and triage practices.”

NEW INFO 8:00 pm (Feb 6):  Extremely concerning news that 3 of 5 confirmed nCoV2019 cases in France are in intensive care, including a couple in their early 30s. While it appears that many cases (around the world) are not much worse than a cold, some others become extremely serious, even for healthy young adults. Why? No one knows.

NEW INFO 1:00 pm (Feb 6):  Kaiyuan Sun and colleagues estimate relative risk of nCoV2019 as a function of age. Strikingly lower risk for kids and youngest adults. h/t Richard Neher.

NEWS 11:00 am (Feb 6):  One of the first doctors to recognize the nCoV2019 outbreak, and reprimanded by police in Wuhan for trying to inform the medical community about this new danger, has himself died from the virus.  RIP, Doctor Li Wenliang.

NEWS 3:00 pm (Feb 5): Wisconsin has reported its first confirmed case of nCoV2019. Interestingly, it involves someone who was infected in Beijing, but not Wuhan or the Hubei province.

NEWS 12:30 pm (Feb 5): The US FDA has granted permission to the various state public-health laboratories to test for the new coronavirus, nCoV2019. Previously, only the CDC could perform the tests. State labs will also send samples to the CDC for confirmation tests.

UPDATE 11:05 am (Feb 4): It’s now reported by expert Helen Branswell that the traveler from Wuhan who infected co-workers in Germany was not entirely asymptomatic, contrary to the Lancet paper. [See New Info from 5:00 pm on Jan 29 below for discussion of that paper.] Still, her symptoms were not obvious to others, and with the potential for hidden transmission chains, that seems pretty important. Moreover, that same paper reported that first German who was infected had a very high viral load in his sputum even after he seemed to have recovered, again suggesting the potential for hidden transmissions.

NEWS 10:30 am (Feb 4): Several new cases in Hong Kong do not have recent travel history to mainland China, nor other obvious connections to travelers. According to Chuang Shuk-kwan, head of the Centre for Health Protection’s communicable disease branch: “It is highly probably the four cases were infected locally, so there could be invisible chains of infection happening within communities … We do not rule out a large spread [of the virus] in the future.”

NEWS 10:20 am (Feb 4): There are some reports of travel-related nCoV2019 cases that do not involve travel to or from China. According to this tweet, a Korean may have been infected in Thailand, and a Malaysian in Singapore.

UPDATE 9:45 am (Feb 4): Richard Neher updates his informative graphs showing growth in cumulative numbers of nCoV2019 cases reported in Hubei (province where Wuhan is located) and elsewhere in China, as well as reported death rates and international travel cases. As Neher notes, there’s much we still don’t know well at all, such as the mortality rate. [See the Update from 6:20 pm on Feb 1 below for some of the complicated issues surrounding even a seemingly simple concept like the death rate.] While it is now clear that many cases are mild, there are also many cases that have not caused death (at least yet), but where the patients are critically ill. To my mind, a huge unknown is whether we will see other hotspots of this coronavirus in China (see News just below) and elsewhere, especially in areas with less developed medical systems and public-health infrastructure, in the weeks and months ahead.

NEWS 11:00 pm (Feb 3):  China closes off another large city, Wenzhou, according to The Straits Times. An east coast city with 9 million residents, Wenzhou is some distance from Wuhan. Only one resident per household can leave home every second day to shop for necessities. Schools are closed until March, and most businesses are shut for two weeks.

UPDATE 10:30 pm (Feb 3):  CDC updated its test results for “people under invesigation” (PUI) — that is, possible cases of nCoV2019 infection. The cumulative totals include 11 positives and 167 negatives, for an overall proportion of 6.2% positive tests. That’s up a bit from the previous report at 5%, although I think the new data includes two secondary infections of spouses.  So not much change. The backlog of pending cases is currently 82, down from 121 in the previous report. With the travel restrictions in place, I would guess that the number of new PUI cases would continue to decline … at least for a while. But we now enter a period of great uncertainty, as the outbreak spreads in China and into other countries.  From the NY Times:

  • “It’s very, very transmissible, and it almost certainly is going to be a pandemic,” said Dr. Anthony S. Fauci, director of the National Institute of Allergy and Infectious Disease. “But will it be catastrophic? I don’t know.”
  • It is “increasingly unlikely that the virus can be contained,” said Dr. Thomas R. Frieden, a former director of the Centers for Disease Control and Prevention who now runs Resolve to Save Lives, a nonprofit devoted to fighting epidemics. “It is therefore likely that it will spread, as flu and other organisms do, but we still don’t know how far, wide or deadly it will be.”

UPDATE 6:20 pm (Feb 1):  Epidemiologist Maia Majumder (@maiamajumder) provides a clear and concise explanation of the different ways of measuring and describing the “deadliness” of infectious diseases. As importantly, she explains why the different estimates change over time. In the case of the population mortality rate, that will tend to increase as an outbreak grows in size, before eventually reversing course once an outbreak has been brought under control. By contrast, the case fatality rate (CFR) reflects the fraction of diagnosed infections that cause death. This latter rate is subject to an early spike because the earliest cases are often the most severe. Also, increased awareness and testing of less severe cases tends to increase over time. Both of these factors mean the CFR typically declines as an outbreak progresses. On the other hand, many serious cases (as reported for nCoV2019 by Chinese health authorities) have not yet resolved. In short, the CFR remains poorly understood at this time. Then, too, there are some people who get an infection, including the nCoV2019 virus, but with no or minimal symptoms, and so they are not diagnosed, These subclinical cases can be discovered later on when populations are surveyed serologically, allowing a further, lower estimate of the fraction of total infections (those discovered later as well as those discovered clinically) that cause death. Dr. Majumder also reminds us that different subpopulations (such as those with chronic diseases) may face different risks.

COMMENT 1:15 pm (Feb 1): There’s plenty of reasons to be worried about this nCoV2019 outbreak, especially if you’re in the most affected areas of China. Hopefully, journalists, scientists, clinicians, and everyone else on social media can communicate and amplify the relevant facts and uncertainties, and avoid sloppy thinking and conspiracy theories.

UPDATE 1:10 pm (Feb 1):  Boom. Trevor Bedford demolishes the claim that the nCoV2019 genome contains bits of HIV sequence, and thereby destroys the conspiracy theory that this corona virus was a bioengineered strain.

UPDATE 12:20 pm (Feb 1): Using his knowledge as an infectious disease specialist combined with common sense, Adam Lauring dismantles the assertion that people might become reinfected with nCoV2019. That’s not to say it’s impossible, but it’s extremely unlikely given such closely related strains (just a few mutations different), and there’s no way of testing that yet. Perhaps something was lost in translation, and the original work only meant to say that nCoV2019 infections increased one’s risk of other secondary infections?

NEW INFO 11:20 am (Feb 1): Simple graphs can reveal a lot. Biophysicist Richard Neher plotted the number of nCoV2019 cases in Hubei and the rest of China over time. Note the log-transformed scale, so a linear trend corresponds to exponential growth. Neher notes some slight decline in the rate of increase, which could mean either that the number of new cases is decelerating (which would be good news) or that the testing capacity is limited and becoming saturated (not good news). Daniel Falush weighed in, suggesting that the number of deaths—alas, not subject to testing limits—would be a better indicator, and he thought that would show signs of slowing. So Neher produced a plot of those data. And yikes: There’s no hint of any slowing, with the number of fatalities doubling about every 3 days. Here’s a screen shot of Neher’s plot:

Corona virus fatalities trajectory from Richard Neher

UPDATE 6:30 pm (Jan 31):  CDC has updated data on the numbers of positive, negative, and pending cases for “patients under investigation.” The proportion of positive tests continues to drop, which is good news:  It’s now 6/120 = 5.0% of the tests of potential nCoV2019 cases in the US that have proven to be positive to date, down from 6.8% a few days ago and 13.5% before that. However, the number of pending (unresolved) cases has continued to increase and now stands at 121.

AN IDEA 11:45 am (Jan 31): For biology teachers at multiple levels (including high school, undergrad, and graduate), this on-going corona virus outbreak could provide valuable information and timely data for teaching core concepts like R0, transmission chains, genomes, phylogenies & more. Students could even analyze and/or interpret new incoming data themselves to draw their own inferences. To see what I mean, check out the superb multi-slide presentation of important concepts, data, and inferences based on the first 42 sequenced genomes of the virus produced and made freely available by the @nextstrain team.

NEW ANALYSIS 10:15 am (Jan 31): The ever-clever Trevor Bedford (say that 10 times fast!) has a twitter thread explaining his new analysis for estimating the probability distribution of the number of new mutations in the viral genome per transmission from one person to another. It relies on knowing the time course of infections and transmissions, as well as the mutation rate, but these parameters are reasonably bounded and becoming better estimated over time. One can also do the inverse analysis to estimate the number of intermediate infections in a transmission chain, which could be useful for epidemiological tracking and investigation.

NEW INFO 10:00 am (Jan 31): New clinical and epidemiologically relevant information on secondary infections in Germany reported in New England Journal of Medicine. h/t @HelenBranswell, who highlights that Patient 1 had “recovered” enough to go to work, but when tested had “a high viral load of 10^8 [100,000,000] copies per milliliter in his sputum.” If this situation is common, it would seem to present a new set of serious challenges.

UPDATE 7:00 pm (Jan 30):The number of international-travel cases continues to increase exponentially, as shown in graph produced by Trevor Bedford. Note the logarithmic y-axis, so a straight line corresponds to exponential growth. With more travel restrictions in place, we would expect that to slow down, even if the epidemic within Wuhan, or China more broadly, continues. So Trevor says he will try to track these data as a function of airplane passengers.

NEW INFO 1:20 pm (Jan 30): First case of transmission within US reported by CDC. Involves a husband and wife, so no evidence of any community-wide spread here in the US to date.

UPDATE 1:00 pm (Jan 30): The @nextstrain team has updated their excellent multi-slide presentation of important inferences based on the first 42 sequenced genomes of the virus. These data continue to point to a single recent origin of the outbreak, with rapid expansion from there. As I noted yesterday, there appear to be clusters of travelers that share one or two mutations, presumably derived from the same intermediate source in the chains of transmissions. These cases, if investigated epidemiologically, might provide valuable clues about the transmission dynamics.

NEW INFO 5:00 pm (Jan 29): I’ve mentioned that we know little about the distribution of outcomes with respect to disease severity. The Lancet (a leading medical journal) has just published a paper analyzing 99 of the early cases in Wuhan, China, that were confiirmed as involving the new nCoV2019 corona virus. These cases involved hospitalization, during a period of considerable stress on the health-care system.  Half of these cases involved patients with other underlying chronic diseases. Nonetheless, about 75% of the patients now have a good prognosis, and about a third have been discharged from the hospital. However, many of the others developed “acute respitory distress syndrome” and 11 of the 99 died. Again, these are atypically severe cases. It’s also very interesting that almost half of all the cases involved individuals who worked at the Wuhan seafood market.  However, the earliest cases appear to not involve that market, which makes this association rather curious.  Also, we still don’t know much about the infections that are less severe for individuals, but which are nonetheless very important for understanding the viral transmission dynamics.

NEW INFO 6:10 pm (Jan 29): From Richard Neher via twitter, a report that 4 Germans who tested postitive after contact with a work-place visitor from Wuhan are apparently asymptomatic. It’s unclear from the short tweet whether the Germans never exhbited any symptoms at all, or only very mild ones, but they are now recovered. This finding supports the conclusion that there are many mild infections (good news), but it also implies that the number of infected people–some of whom might transmit the virus–is quite large (bad news), in line with some of the calculations of the number infected.

NEW INFO 5:20 pm (Jan 29): More nCoV2019 viral genomes have been sequenced from around the world and placed in their phylogenetic context by the @nextstrain team.  I’ve copied a screenshot below that shows the latest version, plotted in terms of mutational distance from the earliest Wuhan samples. It’s very interesting that there are clusters of some of the international-travel cases including (about 2/3 of the way down) the French (2 identical isolates), one from the USA, and a Taiwan case. They all share one mutation that none of the other isolates have. That might not seem like much, but with so few mutations in total (over the whole tree), it strongly suggests that these cases all have some secondary (or later) source in common along the viral transmission chain.  And just above that cluster is another cluster of isolates that all share 2 mutations with an interesting mix of international and non-Wuhan Chinese samples. So there might be some useful epidemiological clues in there, if this information can be coupled with careful studies of patient travel and contacts. 

nCoV2019 nextrstrain phylogram

NEW INFO 12:45 pm (Jan 29): Physicist Dirk Brockmann presents an analysis that uses actual worldwide travel data to estimate the relative probabilities (“import risks”) that travelers from Wuhan enter other countries via specific airports. The results align quite well with where new international cases have been turning up.  [h/t to Richard Neher @richardneher via twitter]

NEW INFO 12:15 pm (Jan 29):  I had read some discussions on the web that the nCoV2019 outbreak might be caused by a “recombinant” virus. This, in turn, led to some conspiracy-type speculation about a virus that escaped from a lab. Recombinants can occur naturally, as well as be made in the lab. So I wondered whether there was good evidence for recombination in these viruses and, if so, whether the recombination pre-dated or post-dated the split between the most closely related bat strain and the Wuhan strain nCoV2019. I turned, once again, to expert Trevor Bedford (@trvrb), since this is a phylogenetically based question. He pointed me to in-depth analyses and discussion of these issues among experts.  First, some genetic recombination has occasionally occurred in these viruses in nature. Second, he sees no evidence of “recombination in the ~50 years since the ancestor of nCoV outbreak viruses split from RaTG13” (i.e., the most closely related corona virus in the data base, which derives from a bat in the year 2013).

NEW INFO 11:00 am (Jan 29):  A bit of good news from CDC. Now 5/73 = 6.8% tests of potential nCoV2019 cases in US have been positive to date. It had been 5/37 = 13.5% at last update. That means no new confirmed positive cases in US. As expected given the spreading infection and expanding concern, the number of pending (unresolved) cases has increased.

NEW INFO 2:00 pm (Jan 27):  On Twitter @afferent_input found monthly data on visitors to US from China. Seasonal data suggest number of travelers in this period might be ~2X higher than my crude estimate, which would reduce the inferred infection proportion and numbers relative to my initial estimates. This factor and the other new info posted [just below] might roughly cancel. Again, all of this information is rough and crudely extrapolated. And none of it bears on critical issues of distribution of severity of infections, etc. 

NEW INFO 1:20 pm (Jan 27):   Here’s another interesting (and concerning) bit of data from the CDC. So far, 37 cases have been investigated.  Of these, 5 have been positive, and 32 negative. However, there are 73 more cases with pending test results in just this first week of CDC data. If the % positive holds in these pending cases, that would triple my estimate of proportion & number infected.  That is, the product (5/37) x 73 suggests that an additional 10 or so infected individuals will be identified as having entered the US in this first week. Again, this is a crude estimate with assumptions, and these potential cases are also presumably in isolation, etc.

Links to my first two posts

Jan 29:  Developing News on the Wuhan Corona Virus, nCoV2019

Jan 27:  Quick-and-Dirty Estimate of Number of nCoV2019 Infections in Wuhan

 

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Evolution goes viral! (And how real science works)

This is the fourth in a series of posts about a new book by Michael Behe, Darwin Devolves. Behe is a leading proponent of intelligent-design creationism (IDC), which asserts that known processes cannot adequately account for evolution and, therefore, some intelligent agent must be involved in the process. Behe is a professor of biochemistry, which gives him knowledge and credentials that most IDC advocates do not have. However, my posts explain why I think his logic is unsound and his evidence weak and biased.

In brief, Behe argues that random mutation and natural selection are almost entirely degradative forces that break or blunt the various functions encoded by genes, producing short-term advantages that are so pervasive that they prevent constructive adaptations, which he claims are very unlikely to emerge in the way that evolutionary biologists have proposed. Unlike young-Earth creationists, Behe accepts the descent of living species from common ancestors over billions of years. To reconcile these seemingly conflicting views, Behe invokes that an intelligent agent (presumably God, though IDC proponents avoid that word so that their ideas might appear to be scientific) has purposefully guided evolution over its long history by somehow inserting new genetic information into chosen lineages along the way. To make his strange argument, Behe works very, very hard to convince readers that standard evolutionary processes are (i) really, really good at degrading functions, and (ii) really, really bad at producing anything new.

In my first post, I explained that Behe’s arguments confuse and conflate what is easy and commonplace over the short run (i.e., mutations that break or blunt functional genes) with the lasting impacts of less frequent but constructive adaptations (i.e., new functions and subsequent diversification) over the long haul of evolution. My second post examined a case involving polar bears, which Behe highlighted as a compelling example of degradative evolution, but where a careful review of the science suggests that gene function improved. Behe also highlighted results from my lab’s long-term evolution experiment with bacteria, but in my third post I explained that he overstates his case by downplaying or dismissing evidence that runs counter to his argument.

In this post, I’ll discuss an experiment that Behe ignores in Darwin Devolves. (Behe clearly knows the work, because he wrote about it on the Discovery Institute’s anti-evolution blog. But as usual, he spun the story to obscure the problems for his arguments, all the while accusing the scientists who collect data to test hypotheses of spinning the story.) In fact, as I’ll explain, the results also undermine the claims in Behe’s two previous books, Darwin’s Black Box and The Edge of Evolution, about the supposed shortcomings of evolution.

(Before presenting this experiment, I want to mention briefly two other papers that readers interested in what else Behe missed or downplayed might want to read. First, Rees Kassen posted a preprint of a paper on “Experimental evolution of innovation and novelty.” He reviews empirical evidence and discusses conceptual issues bearing on the origin of new functional abilities observed in many experiments with bacteria and other microbes. Second, Chris Adami, Charles Ofria, Rob Pennock, and I published a paper over 15 years ago that demonstrated the logical fallacy of Behe’s assertions about irreducible complexity. Behe mentions that paper derisively, without addressing its substance in Darwin Devolves, as follows: “A computer simulation of computer program development that ignores biology entirely.” A more accurate statement would have been: “Computer programs can evolve by random mutation and natural selection the ability to perform complex functions that show the concept of irreducible complexity is total nonsense.” The rest of this post is longer than I planned, because I want to provide background for readers who aren’t microbiologists, and because—like so much of science—it’s an interesting story with unexpected twists and turns along the way.)

IV. Phage lambda evolves a new capability without breaking anything

There are a lot of viruses in the world. Fortunately, most of them don’t infect humans. Many of them infect bacteria, as it so happens. In fact, before antibiotics were used as therapeutic agents, there was hope that bacteriophages (“bacteria eaters”), or phages for short, would be useful in treating diseases. And now, with the evolution of pathogenic bacteria that are resistant to many or all available drugs, researchers are reconsidering the possibility of using phages to treat some infections.

My lab is best known for the long-term evolution experiment (LTEE) with E. coli bacteria. But over the years, my students have also performed other experiments with a variety of microbes, including some viruses that infect E. coli. One of those viruses is called lambda. For decades, lambda was probably the most intensively studied virus on the planet—just as E. coli was a model for understanding bacterial genetics and physiology, lambda became a model for understanding viral genetics and infection.

One reason lambda became a hit was because it has an interesting life cycle. After lambda enters a bacterial cell (and assuming the cell lacks some internal defenses), the virus can do one of two things. It can commandeer the host, hijacking the cellular machinery to produce a hundred or so progeny before bursting the host cell and releasing its “babies” to find new cells to infect. Alternatively, the virus’s DNA may be integrated into the host’s chromosome, hiding out and being replicated alongside the host’s genes—though the virus may later exit the chromosome and reactivate its lethal program. (Pretty neat, and a bit scary, right?)

Well, as cool as that is, it’s not what my student Justin Meyer (now on the faculty at UCSD) was studying. He was using a strain of lambda that can’t integrate into the bacterial cell’s chromosome—a successful infection takes only the first route, killing the cell in the course of making more viruses. Justin was studying this simpler virus because we were interested in whether the evolution of the bacterial hosts in response to the presence of lambda virus might depend on what food we gave the bacteria.

Let’s back up and explain why that might matter. Viruses like lambda don’t just glom onto any part of a bacterial cell; instead, they adsorb to specific receptors on the cell’s surface, with a successful attachment triggering the injection of their DNA into the cell. Lambda recognizes a particular cell-surface protein called LamB. (Despite decades of study of the interaction between lambda and E. coli, including experiments that specifically sought to see whether mutants could exploit other receptors, no one had ever seen lambda use any other receptor.) Of course, E. coli doesn’t make LamB for the sake of the virus. The LamB protein is one of several “porin” proteins that E. coli produces, and which serve as channels to allow molecules, like sugars, to cross the outer cell envelope. (Other proteins transport sugars across the inner cell membrane.) LamB, in particular, is a fairly large channel that allows the sugars maltose and maltotriose to enter the cell. Maltose and maltotriose are made of two and three linked glucose molecules, respectively. Glucose, being smaller, can readily enter a cell via smaller channels. When growing on glucose, E. coli cells don’t bother to produce much LamB protein. However, when cells sense that maltose or maltotriose, but not glucose, is present they activate the gene that encodes LamB. In doing so, however, the cells become more vulnerable to lambda, because that protein serves not only to transport these larger sugars but also as the receptor for the virus.

Coming back to Justin Meyer’s research, we wanted to see how different sugars affected the bacteria’s evolutionary response to lambda. (Justin and I have a paper in press comparing outcomes across the glucose, maltose, and maltotriose environments.) We reasoned that, if the bacteria were fed glucose, they could damage or delete the lamB gene that encodes the LamB protein. If the bacteria mutated the LamB protein, then the virus might counter with a mutation that restored their affinity for the mutated protein; but if the bacteria deleted or otherwise destroyed the LamB protein, we reasoned the virus would go extinct.

However, the first experiment using only the glucose treatment played out differently than what we expected—that’s science, and that’s why you do experiments—and it set Justin’s research off in a new direction. Instead of mutating the lamB gene, the bacteria evolved resistance to the virus by mutating another gene, called malT, that encodes a protein that activates the production of LamB. The viruses didn’t go extinct, however, because there was some residual, low-level expression of the LamB protein. That was enough to keep the viruses going, which also meant they could keep evolving.

To make a long story short, after just 8 days, one of six lambda populations evolved the ability to infect malT-mutated cells by attaching to a different surface protein, one called OmpF (short for outer membrane protein F). This evolved lambda virus could now infect E. coli cells through the original receptor, LamB, or this new one, OmpF. It had gained a new functional capability.

To understand this change, Justin sequenced the genome of this virus. He found a total of 5 mutations compared to the lambda virus with which he had begun. All 5 mutations were in the same gene, one that encodes the J protein in the “tail” of the virus that interacts with the cell surface. He also sequenced the J gene for some other viruses isolated from the same population. He found one virus that had 4 of these 5 mutations, but which could not infect cells via the OmpF receptor. Did that mean that only one of the 5 mutations was necessary to evolve this new function?

As it turns out, the answer is no. To better understand what had happened, Justin scaled up his experiments and ran an additional 96 replicates with lambda, E. coli, and glucose. In 24 cases, the viruses evolved the new mode of infection within three weeks. Justin sequenced the J gene from the viruses able to target OmpF in those 24 cases, and in 24 other cases where the virus could still use only the LamB receptor. He found that all 24 with the new capability had at least 4 mutations; these included 2 changes that were identical in all 24 lines, a third that further mutated one of the same codons (sets of 3 DNA bases that specify a particular amino acid to be incorporated into a protein), and another mutation that was always within a span of 11 codons. All of these mutations cause amino-acid substitutions near the end of the J protein, which is known to interact with the LamB receptor. The J protein is over 1100 amino acids in length, and so this concentration and parallelism (repeatability across lineages) is striking and strongly implies that natural selection favored these mutations.

Remember, too, that nothing is broken. These viruses can now use both the original LamB receptor and the alternative OmpF receptor. (This fact was demonstrated by showing that the viruses can grow on two different constructed hosts genotypes, one completing lacking LamB and the other completely lacking OmpF.)

None of the 24 viruses that had not evolved the ability to use the OmpF receptor had all 4 of these mutations. However, three of them shared 3 of the 4 mutations with viruses that had acquired that new ability. And yet, none of those had any capacity to grow on cells that lacked the LamB receptor. In other words, the set of all 4 of these mutations was needed to produce this new ability—no subset could do the job. (We initially lacked one of the four possible viral genotypes having each subset of 3 mutations. Later work confirmed that all four mutations are required.)

At first glance, it seems like none of the viral lineages should have been able to acquire all 4 mutations, at least if you accept the flawed reasoning from Behe’s previous book on The Edge of Evolution. If you need all 4 mutations for the new function, so the thinking goes, and if none of them provide any degree of that function, then you would need all 4 mutations to occur in one lineage by chance, which is extremely unlikely. (How unlikely is difficult to calculate precisely. To get some inkling, none of the 48 sequenced J genes—including both those that did and did not evolve the new capability—had even one synonymous mutation. Synonymous mutations don’t change the amino-acid sequence of an encoded protein, and so they provide a benchmark for the accumulation of selectively neutral mutations.)

And yet, 24 of the 96 lineages did just that—they evolved the new ability, and in just a few weeks time. If you’re into intelligent design, then I guess you’d have to conclude that some purposeful agent was pretty darn interested in helping the viruses vanquish the bacteria. If you’re a scientist, though, you’re trained to think more carefully and look for natural explanations—ones that you can actually test.

So how could 4 mutations arise so quickly in the same lineage? Natural selection. But wait, didn’t Justin find that all 4 of those mutations were required for the virus to exploit the new OmpF receptor? Yes, he did.

Our hypothesis was that the mutations that set the stage for the virus to evolve the ability to target OmpF were beneficial because they improved lambda’s ability to use its original LamB receptor. But wait, that’s the receptor they’ve always used. Shouldn’t they already be perfectly adapted to using that receptor? How can there be room for improvement?

If you’ve read my posts on polar bears and bacteria, you’ve probably got the idea. When the environment changes, all bets are off as to whether a function is optimally tuned to the new conditions. Lambda did not evolve in the same medium where Justin ran his experiments; and while lambda certainly encountered E. coli and the LamB receptor in its history, the cell surfaces the virus had to navigate in nature were more heterogeneous than what they encountered in the lab. In other words, there might well be scope for the viral J protein to become better at targeting the LamB receptor under the new conditions.

To an evolutionary biologist, this hypothesis is so obvious, and the data on the evolution of the J protein sequence so compelling, that it scarcely needs testing. Nonetheless, it’s always good to check one’s reasoning by collecting new data, and another talented student joined the project who did just that. Alita Burmeister (now a postdoc at Yale) competed lambda strains with some (but not all) of the mutations needed to use OmpF against a lambda strain that had none of those mutations. She studied six “intermediate” viruses, each of them isolated from an independent population that later evolved the ability to use OmpF.

Alita ran two sets of competitions between the evolved and ancestral viruses. In one set, the viruses fought over the ancestral bacterial strain; in the other set, they competed for a bacterial strain that had previously coevolved with lambda and become more resistant to infection. Four of the six evolved intermediate viruses outcompeted their ancestor for the naïve bacteria, and all six prevailed when competing for the tough-to-infect coevolved host cells. Alita ran additional experiments showing that the intermediates were better than the ancestral virus at adsorbing to bacterial cells—the precise molecular function that the J protein serves. These results clearly support the hypothesis that the first few mutations in the evolving virus populations improved their ability to infect cells via the LamB receptor.

Natural selection did its thing, in other words, discovering mutations that provided an advantage to the viruses. Some of the resulting viruses—those with certain combinations of three mutations—just happened to be poised in the space of possible genotypes such that a fourth mutation gave them the new capacity to use OmpF.

Now let’s step back and think about what this case says about the validity of the arguments that Behe has made in his three books.

Anybody remember Behe’s first book, Darwin’s Black Box, published in 1996? There, Behe claimed evolution doesn’t work because biological systems exhibit so-called “irreducible complexity,” which he defined as “… a single system composed of several well-matched, interacting parts that contribute to the basic function, wherein the removal of any one of the parts causes the system to effectively cease functioning.” Evolution can’t explain these functions, according to Behe, because you need everything in place for the system to work. Strike one! Lambda’s J protein required several well-matched, interacting amino acids to enable infection via the host’s OmpF receptor. Removing any one of them leaves the virus unable to perform that function. (Alas, Behe’s argument wasn’t merely mistaken, it also wasn’t new—since Darwin, and as explained in increasing detail by later biologists, we’ve known that new functions evolve by coopting and modifying genes, proteins, and other structures that previously served one function to perform a new function.)

The Edge of Evolution, Behe’s second book, claimed that evolution has a hard time making multiple constructive changes, implying the odds are heavily stacked against this occurring. Strike two!! Lambda required four constructive changes to gain the ability to use OmpF, yet dozens of populations in tiny flasks managed to do this in just a few weeks. That’s because the intermediate steps were strongly beneficial to the virus, so that each step along the way proceeded far faster than by random mutation alone.

Darwin Devolves says that adaptive evolution can occur, but that it does so overwhelmingly by breaking things. Strike three!!! The viruses that can enter the bacterial cells via the OmpF receptor are not broken. They are still able to infect via the LamB receptor and, in fact, they’re better at doing so then their ancestors in the new environment. (In his blog post after our paper was published in Science, Behe used the same sleight of hand he used to downplay the evolution of the new ability to use citrate in one LTEE population. That is, Behe called lambda’s new ability to infect via the OmpF receptor a modification of function, instead of a gain of function, based on his peculiar definition, whereby a gain of function is claimed to occur only if an entirely new gene “poofs” into existence. However, that’s not the definition of gain-of-function that biologists use, which (as the term implies) means that a new function has arisen. That standard definition aligns with how evolution coopts existing genes, proteins, and other structures to perform new functions. Behe’s peculiar definition is a blatant example of “moving the goalposts” to claim victory.)

As Nathan Lents, Joshua Swamidass, and I wrote in our book review, “Ultimately, Darwin Devolves fails to challenge modern evolutionary science because, once again, Behe does not fully engage with it. He misrepresents theory and avoids evidence that challenges him.”

If you’ve followed the logic and evidence in the three systems I’ve written about—polar bears adapting to a new diet, bacteria fine-tuning and even evolving new functions as they adapt to laboratory conditions, and viruses evolving a new port of entry into their hosts—you’ll understand why Behe’s arguments against evolution aren’t taken seriously by the vast majority of biologists. As for Behe’s arguments for intelligent design, they rest on his incredulity about what evolution is able to achieve, and they make no testable predictions about how the designer intervenes in the evolutionary process.

[The images below show infection assays for 4 lambda genotypes on 2 E. coli strains. The dark circles are “plaques”—areas in a dense lawn of bacteria where the cells have been killed by the virus. The viruses (labeled at bottom) include the ancestral lambda virus and 3 evolved genotypes. One bacterial strain expresses the LamB receptor (top row), while the other lacks the gene that encodes LamB (bottom row). All 4 viruses can infect the cells that produce LamB, but only the “EvoC” virus is able to infect the cells without that receptor. Images from Meyer et al., 2012, Science paper.]

Lambda plaque assays

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Is the LTEE breaking bad?

Michael Behe has written a third book, Darwin Devolves, that continues his quixotic effort to overturn evolutionary biology. Nathan Lents, Joshua Swamidass, and I wrote a book review for Science. (You can find an open-access copy here.) As our short review states, there are indeed many examples of evolution in which genes and their functions have been degraded, sometimes conferring an advantage to the organism. However, Behe’s book largely ignores the ways by which evolution generates new functions. That’s a severe problem because Behe uses the evidence for the ease of gene degradation to support his claim that our current understanding of the mechanisms of evolution is inadequate.

This is my third in a series of posts delving into various issues where I think Behe’s logic and evidence are weak. These weaknesses undermine his position that the known mechanisms of evolution are inadequate to explain life as we see it in the fossil record and in the diversity of living species. Let me be clear: there is still much to learn about the intricacies of how evolution works, both in terms of a better understanding of the general mechanisms and unraveling all the fascinating particulars of what happened along various lineages. However, I don’t see much chance of future research upending the central role of natural selection—operating over vast time along with mutation, drift, and recombination (including various forms of horizontal gene transfer)—in creating new functions that spark the diversification of life. By contrast, Behe accepts that natural selection occurs, but he treats it almost entirely as a degradative process that weakens and destroys functions. To explain all the new functions that have arisen during evolution (and he accepts the fact that evolution has occurred for billions of years), Behe appeals to an “intelligent agent” who somehow, mysteriously has added new genetic information into evolving lineages.

In my first post, I explained why Behe’s “first rule of adaptive evolution” doesn’t imply what he says it does about evolution writ large. In particular, his overarching thesis confuses frequency over the short run with lasting impact over the long haul of evolution. In my second post, and building on the work of others, I examined a specific case involving polar bears, which Behe argued showed adaptations resulting from degradative evolution. He apparently regarded the case as so compelling that he used it as the lead example in his book, but a careful review of the science suggests an alternative explanation, in which gene function actually improved.

In this post, I examine Behe’s interpretation of findings from a long-term evolution experiment (LTEE) with E. coli bacteria that has been running in my lab for over 30 years. In short, the LTEE represents an ideal system in which to observe degradative evolution, and indeed we’ve seen examples of such changes. However, Behe overstates his case by downplaying or dismissing evidence that runs counter to his thesis.

III. Evolution of functionality in the LTEE

Recall what Behe calls “the first rule of adaptive evolution: break or blunt any functional gene whose loss would increase the number of a species’ offspring.” In support of that rule, Darwin Devolves pays considerable attention to the LTEE. Behe skillfully uses it to build his case that unguided evolution produces adaptations (almost) exclusively by breaking or blunting functional genes. The implication is that constructive adaptations—those that do not involve breaking or blunting genes—require an “intelligent agent” who has introduced new genetic information, by some mysterious process, into certain lineages over the course of life’s history.

Am I surprised that Behe uses the LTEE as one of the centerpieces of Darwin Devolves? No, not at all. Does the LTEE provide strong support for his argument? No, it does not. The LTEE fits the bill for Behe because it’s just about the best case possible to showcase his rule. But just as loss of sight in cave-dwelling organisms is a special case that won’t tell us how eyes evolved, one must be careful when extrapolating from this experiment to evolution writ large. (I say this even though the LTEE is my scientific “baby” and has been a useful model system for studying some aspects of evolution.)

The LTEE was designed (intelligently, in my opinion!) to be extremely simple in order to address some basic questions about the dynamics and repeatability of evolution, while minimizing complications. It was not intended to mimic the complexities of nature, nor was it meant to be a test-bed for the evolution of new functions. The environment in which the bacteria grow is extremely simple. The temperature is kept constant at 37C, the same as our colons where many E. coli live. The LTEE “host” is an Erlenmeyer flask, not an animal with an immune system and other defenses. There are no antibiotics present, no competing species, and no viruses that plague bacteria in nature. And the culture medium contains a single source of energy that the ancestral bacteria can use, namely the sugar glucose. In contrast, E. coli lineages have endured and adapted over millions of years to countless combinations of resources, competitors, predators, toxins, and temperatures in nature.

Indeed, the LTEE environment is so extremely simple that one might reasonably expect the bacteria would evolve by breaking many existing functions. That is because the cells could, without consequence, lose their abilities to exploit resources not present in the flasks, lose their defenses against absent predators and competitors, and lose their capacities to withstand no-longer-relevant extreme temperatures, bile salts, antibiotics, and more. The bacteria might even gain some advantage by losing these functions, if doing so saved time, energy, or materials that the cells could better use to exploit the limited glucose supply.

And just as one would expect, the bacteria have diminished or lost various abilities during the LTEE. For example, all 12 populations lost the ability to use another sugar, called ribose, and they gained a small but measurable competitive advantage as a result. Similarly, half of the lines evolved defects in one or another of their DNA repair systems, which led to hypermutability. While hypermutability resulted from a loss of function at the molecular level, it produced a slight gain in terms of the rate at which those lineages adapted to their new laboratory environment. There are undoubtedly many functional losses that have occurred during the LTEE, some that have been described and others not.

If that was all there were to the story, I might say that Behe’s portrayal was correct, but that he had missed the point—namely, that of course evolution often involves the loss of functions that are no longer useful to the organism. Biologists have known and understood this since Darwin.

But there is more to evolution than that, not only in nature but, as it turns out, even in the simple world of the LTEE. We’ve discovered cases where beneficial mutations evolved in genes that encode proteins that are essential, not dispensable, including ones involved in synthesis of the cell envelope and in structuring DNA so that it can be copied, transcribed, and packed into the tiny space of a cell. We’ve also found genes in which mutations occur repeatedly near key interfaces of the encoded proteins, in ways that imply the fine-tuning of protein functions to the LTEE environment, rather than degradation or loss of those functions.

In Darwin Devolves, Behe asserts (p. 344) that “it’s very likely that all of the identified beneficial mutations worked by degrading or outright breaking the respective ancestor genes.” He includes a footnote that acknowledges our work that suggests the fine-tuning of some protein functions, but there he writes (p. 609): “More recent investigation by Lenski’s lab suggests that mutations in a small minority (10 of 57) of selected E. coli genes may not completely break them but rather, as they put it, ‘fine-tune’ them (probably by degrading their functions).” Why does Behe assert that fine-tuning of genes occurred “probably by degrading their functions”?

Perhaps it’s because this assertion supports his claim, but more charitably I suspect the underlying reason is similar to the problematic inferences that got Behe into trouble in the case of the polar bear’s genes. That is, if one assumes the ancestral state of a gene is perfect, then there’s no room for improvement in its function, and the only possible functional changes are degradative. In my post on the polar bear case, I explained why the assumption that a gene is perfect (or nearly so) makes sense in certain situations. However, that assumption breaks down when an organism encounters a new environment, where the optimal state of a protein might differ from what it was before. Perhaps, for example, a mutation that would have slightly reduced an essential protein’s activity in the ancestral environment slightly improves its activity in the new environment. As I explained earlier, the LTEE environment differs from the conditions that E. coli experienced before being brought into the lab. It would be surprising if some proteins couldn’t be fine-tuned such that their activities were improved under the particular pH, temperature, osmolarity, and other conditions of the LTEE. It is unreasonable to simply assume that fine-tuning mutations “probably” degrade functions when evolving populations—whether of bacteria or bears—encounter new conditions.

The adaptation in the LTEE that has garnered the most public attention, though, is far less subtle. (The attention grew enormously after I had an email exchange with Andrew Schlafly, who runs the “Conservapedia” website.) After more than 30,000 generations, one of the 12 lines evolved the ability to consume citrate in an oxygen-rich environment—something that E. coli normally cannot do. Citrate, it turns out, has been a potential source of carbon and energy in the culture medium ever since the LTEE started. (The citrate is there, despite the inability of E. coli to import it from the medium, because it chelates iron and, in so doing, makes that micronutrient available to the cells.)

Sequencing the genomes of the citrate-using lineage revealed an unusual mutation—a physical rearrangement that brought together regulatory and protein-coding sequences in a new way—and genetic experiments demonstrated that mutation was responsible for this gain of function. In the line that gained the ability to consume citrate, the rearrangement involved duplicating a particular DNA segment; additional experiments showed that other types of rearrangements could also generate this ability. Even now, after more than 70,000 generations, none of the other LTEE populations has managed to evolve this new ability, despite its great benefit to the bacteria. This difficulty reflects several factors: (i) the low rate of occurrence of the necessary rearrangement mutations; (ii) the fact that efficient use of citrate requires certain additional mutations; and (iii) the absence of other, more highly beneficial mutations that could out-compete early, weakly beneficial citrate-using mutants.

To his credit, Behe does write about the lineage that evolved the ability to consume the citrate. However, he dismisses it as a “sideshow” (p. 365), because he refuses to call this new capability a gain of function. Instead, Behe writes (p. 362) that under his self-fulfilling scheme “the mutation would be counted as modification-of-function—because no new functional coded element was gained or lost, just copied.” In other words, Behe won’t count any newly evolved function as a gain of function unless some entirely new gene or control region “poofs” into existence.

But that’s not how evolution works—unless you believe, as Behe apparently does, that God or some other “intelligent agent” intervened to insert new genetic information into various lineages during the course of history. (Suffice it to say that I don’t regard this as a scientifically useful hypothesis, because I don’t think it can be tested.) Evolutionary biology doesn’t require that new genes poof into existence. Instead, old genes and their products are coopted, modified, and used in new ways—a process called exaptation. For example, crystallin proteins in the lenses of our eyes derive from proteins that performed other functions. At a larger physical scale, the wings of birds and bats derive from the forelimbs of their four-legged ancestors, which in turn derive from fins of fishes.

In short, Darwin Devolves presents a biased picture of the LTEE’s findings. Behe is overly confident in asserting that the vast majority of beneficial mutations have degraded functions, when the functional effects of most of these mutations have not been measured under relevant conditions. In any case, the experiment was designed to address issues other than molecular functionality, with the environment deliberated constructed to be as simple as possible. And yet, having closed the door on nearly all opportunities for new functions to evolve, a striking example arose in a tiny flask after a mere decade or two.

[This image shows some of the LTEE populations in their flasks. The one in the center is more turbid because the bacteria have reached a higher density after they evolved the ability to consume citrate in the culture medium.  Photo credit: Brian Baer and Neerja Hajela.]

LTEE lines centered on citrate #11

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Does Behe’s “First Rule” Really Show that Evolutionary Biology Has a Big Problem?

Michael Behe has a new book coming out this month called Darwin Devolves. Nathan Lents, Joshua Swamidass, and I wrote a review of that book for the journal Science. (You can also find an open-access copy of our review here.) It provides an overview of the problems we see with his thesis and interpretations. As our review states, Behe points to many examples of evolution in which genes and their functions have been degraded, but he largely ignores the ways that evolution generates new functions and thereby produces complexity. That’s a severe problem because Behe uses the evidence for the ease of gene degradation to support his overarching implication that the current scientific understanding of the mechanisms of evolution is inadequate and, consequently, the field of evolutionary biology has a “big problem.”

I won’t attempt to summarize Behe’s entire book nor our short review, as people can read those for themselves if they want. Instead, I hope to accomplish three things in this post and two more that will follow. In this first post, I explain why Behe’s so-called “first rule of adaptive evolution” does not imply what he says it does about evolution writ large. In the second post, I’ll discuss whether my long-term evolution experiment (the LTEE for short) does or doesn’t provide strong support for Behe’s position in that regard. In my third post, I’ll explain why I think that Behe’s positions, taken as a whole, are scientifically untenable.

I. Behe’s “First Rule of Adaptive Evolution” Confounds Frequency and Importance

Behe’s latest book is centered around what he calls “The First Rule of Adaptive Evolution: Break or blunt any gene whose loss would increase the number of offspring.” As he wrote in an immediate, dismissive response to our review: “The rule summarizes the fact that the overwhelming tendency of random mutation is to degrade genes, and that very often is helpful. Thus natural selection itself acts as a powerful de-volutionary force, increasing helpful broken and degraded genes in the population.”

Let’s work through these two sentences, because they concisely express the thrust of Behe’s book. The first sentence regarding “the tendency of random mutation” is not too bad, though it is overly strong. I would tone it down as follows: “The tendency of random mutation is to degrade genes, and that is sometimes helpful.” My reasons for these subtle changes are that: (i) many mutations are selectively neutral or so weakly deleterious as to be effectively invisible to natural selection; (ii) while loss-of-function mutations are sometimes helpful to the organism, I wouldn’t say that’s “very often” the case (though it may be in some systems, as I’ll discuss in part II); and (iii) even those degradative mutations that are not helpful on their own sometimes persist and occasionally serve as “stepping stones” on the path toward new functionality. This last scenario is unlikely in any particular instance, but given the prevalence of degrading mutations it may nonetheless be important in evolution. (This scenario does not fit neatly within the old-fashioned caricature of Darwinian evolution as only proceeding by strictly adaptive mutations, but it is certainly part of modern evolutionary theory.)

Behe’s next sentence then asserts the power of the “de-evolutionary” process of gene degradation. This is an unjustifiable extrapolation, yet it is central to Behe’s latest book. (It’s not the sort of error I would expect from anyone who is deeply engaged in an earnest effort to understand evolutionary science and present it to the public.) Yes, natural selection sometimes increases the frequency of broken and degraded genes in populations. But when it comes to the power of natural selection, what is most frequent versus most important can be very different things. What is most important in evolution, and in many other contexts, depends on timescales and the cumulative magnitude of effects. As a familiar example, some rhinoviruses are the most frequent source of viral infections in our lives (hence the expression “common cold”), but infections by HIV or Ebola, while less common, are far more consequential.

Or consider an investor who bought stocks in 100 different companies 25 years ago, of which 80 have been losers. Ouch? Maybe not! A stock can’t lose more than the price that was paid for it, and so 20 winners can overcome 80 losers. Imagine if that investor had picked Apple, for example. That single stock has increased in value by well over 100-fold in that time, more than offsetting even 80 total wipeouts all by itself. (In fact, research on the stock market has shown the vast majority of long-term gains result from a small minority of companies that, like Apple, eventually become big winners.)

In the same vein, even if many more mutations destroy functions than produce new functions, the latter category has been far more consequential in the history of life. That is because a new function may enable a lineage to colonize a new habitat or realm, setting off what evolutionary biologists call an “adaptive radiation” that massively increases not only the numbers of organisms but, over time, the diversity of species and even higher taxa. As one example, consider Tiktaalik or some relative thereof, in any case a transitional kind of fish whose descendants colonized land and eventually gave rise to all of the terrestrial vertebrates—amphibian, reptiles, birds, and mammals. That lineage left far more eventual descendants (including ourselves), and was far more consequential for the history of life on Earth, than 100 other lineages that might have gained a transient advantage by degrading some gene and its function before eventually petering out.

Asteroid impacts aren’t common either, but the dinosaurs (among other groups) sure felt the impact of one at the end of the Cretaceous. (There remains some debate about the cause of that mass extinction event, but whatever the cause its consequences were huge.) Luckily for us, though, some early mammals survived. Evolution often leads to dead ends, sometimes as a consequence of exogenous events like asteroids, and other times because adaptations that are useful under a narrow set of conditions (such as those caused by mutations that break or degrade genes) prove vulnerable over time to even subtle changes in the environment. It has been estimated that more than 99% of all species that have ever existed are now extinct. Yet here we are, on a planet that is home to millions of diverse species whose genomes record the history of life.

Summing up, Behe is right that mutations that break or blunt a gene can be adaptive. And he’s right that, when such mutations are adaptive, they are easy to come by. But Behe is wrong when he implies these facts present a problem for evolutionary biology, because his thesis confuses frequencies over the short run with lasting impacts over the long haul of evolution.

[The picture below shows the Tiktaalik fossil discovered by Neil Shubin and colleagues.  It was posted on Wikipedia by Eduard Solà, and it is shown here under the indicated Creative Commons license.]

Tiktaalik

 

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Asking for a Skeptic Friend

I sometimes get email from people asking, in one way or another, whether our long-term evolution experiment (LTEE) with E. coli provides evidence of evolution writ large – new species, new information, or something of that sort. I try to answer these questions by providing some examples of what we’ve seen change, and by putting the LTEE into context. Here’s one such email:

Hi Professor Lenski,

I have a quick question. I’m asking because I am having a discussion with someone who is skeptical of evolution. The question is: Over the 50,000 generations of e-coli has any of the e-coli evolved into something else or is it still e-coli?

I am a non-religious person who likes to think of myself as an adherent to science but I am not sure how to respond to my skeptic-friend.

Thank you!

And here’s my reply:

Hello —-,

50,000 generations, for these bacteria, took place in a matter of ~25 years. They have changed in many (mostly small) ways, and remained the same in many other respects, just as one expects from evolutionary theory. Although these are somewhat technical articles, I have attached 3 PDFs that describe some of the changes that we have seen.

Wiser et al. (2013) document the process of adaptation by natural selection, which has led to the improved competitive fitness of the bacteria relative to their ancestors.

Blount et al. (2012) describe the genetic changes that led one population (out of the 12 in the experiment) to evolve a new capacity to grow on an alternative source of carbon and energy.

Tenaillon et al. (2016) describe changes that have occurred across all 12 populations in their genomes (DNA sequences), which have caused all of them to become more and more dissimilar to their ancestor as time marches on.

Best wishes,

     Richard

Although these articles were written for other scientists, they make three big points that I hope almost anyone with an open mind can understand.

  • We see organisms adapting to their environment, as evidenced by increased competitiveness relative to their ancestors.
  • Against this backdrop of more or less gradual improvement, we occasionally see much bigger changes.
  • And at the level of their genomes, we see the bacteria becoming more and more different from their ancestors.

In these fundamental respects, evolution in these flasks works in much the same way that evolution works in nature. Of course, the scales of time and space are vastly greater in nature than they are in the lab, and natural environments are far more complex and variable than is the simple one in the LTEE. But the core processes of mutation, drift, and natural selection give rise to evolution in the LTEE, just as they do (along with sex and other forms of gene exchange) in nature.

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On the Evolution of Citrate Use

Those who follow the long-term evolution experiment (LTEE) with E. coli know that the most dramatic change we have observed to date is the origin of the new ability to grow on citrate. It’s dramatic for several reasons including the fact (external to the LTEE) that E. coli has been historically defined as a species based in part on its inability to grow on citrate in oxic environments and the fact (internal to the LTEE) that it was so difficult for the bacteria to evolve this ability that only one of the populations did so, and that it took over 30,000 generations even though an abundance of citrate has been present in the medium throughout the LTEE. Even after 64,000 generations, only the Ara–3 population has evolved that new ability.

Zachary Blount, formerly a graduate student and now a postdoc in my lab, has spent the last decade studying the evolution of this population and its new ability. His two first-authored papers in PNAS (2008) and Nature (2012) demonstrated, respectively, that (i) the origin of the ability to grow on citrate in the LTEE was contingent on one or more “potentiating” mutations that happened before the “actualizing” mutation that conferred the new function first appeared, and (ii) the actualizing mutation was a physical rearrangement of the DNA that brought together a structural gene, citT, that encodes a transporter and a previously unconnected regulatory region to generate a new module that caused the phenotypic transition to Cit+. These papers presented and discussed much more than these two points, of course, but they are the key findings. More recently, Zack was a coauthor on a paper in eLife (2015) by Erik Quandt, Jeff Barrick, and others that identified two mutations in the gene for citrate synthase—one that potentiated the evolution of citrate utilization, and another that subsequently refined that new function.

So we were keenly interested when we saw a new paper titled “Rapid evolution of citrate utilization by Escherichia coli by direct selection requires citT and dctA” by Dustin Van Hofwegen, Carolyn Hovde, and Scott Minnich. The paper is posted online as an accepted manuscript by the Journal of Bacteriology. What follows here are some overall impressions of their paper that Zack and I put together. We may follow these impressions later with some further analysis and comments.

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Let’s begin by saying that it’s great to see other groups working on interesting systems and problems like the evolution of citrate utilization in E. coli.

Moreover, the actual science that was done and reported looks fine and interesting, though we have a few quibbles with some details that we will overlook for now. By and large, the work confirms many of the findings that were reported in our papers cited above:

(i) the ability to grow on citrate in the presence of oxygen can and does evolve in E. coli (Blount et al., 2008);

(ii) when aerobic growth on citrate evolves, it does not do so quickly and easily (Blount et al., 2008) but instead takes weeks or longer—more on that below;

(iii) all strains that have evolved this new ability have physical rearrangements that involve the citT gene and appear also to involve a so-called “promoter capture” whereby a copy of this transporter-encoding gene acquires a new upstream regulatory region (Blount et al., 2012); and

(iv) genetic context matters—the strain one uses affects the likelihood of evolving the Cit+ function (Blount et al., 2008) and the resulting ability to grow on citrate (Blount et al., 2012; Quandt et al., 2015).

The problem, then, is not with the experiments and data. Rather, the problem is that the results are wrapped in interpretations that are, in our view, flawed and fallacious.

“No new genetic information”

The authors assert repeatedly (last sentence of their Importance statement, and first and last paragraphs of their Discussion) that “no new genetic information evolved.” However, that statement flatly contradicts the fact that in their experiments, and ours, E. coli gained the new ability to grow on citrate in the presence of oxygen. We would further add (which we have not emphasized before) that these Cit+ strains can grow on citrate as a sole carbon source—when E. coli grows anaerobically on citrate, it requires a second substrate for growth in order to use the citrate (a phenomenon called “co-metabolism”).

The claim that “no new genetic information evolved” is based on the fact that the bacteria gained this new ability by rearranging existing structural and regulatory genetic elements. But that’s like saying a new book—say, Darwin’s Origin of Species when it first appeared in 1859—contains no new information, because the text has the same old letters and words that are found in other books.

In an evolutionary context, a genome encodes not just proteins and patterns of expression, but information about the environments where an organism’s ancestors have lived and how to survive and reproduce in those environments by having useful proteins, expressing them under appropriate conditions (but not others), and so on. So when natural selection—that is, differential survival and reproduction—favors bacteria whose genomes have mutations that enable them to grow on citrate, those mutations most certainly provide new and useful information to the bacteria.

That’s how evolution works—it’s not as though new genes and functions somehow appear out of thin air. As the bacterial geneticist and Nobel laureate François Jacob wrote (Science, 1977): “[N]atural selection does not work as an engineer works. It works like a tinkerer—a tinkerer who does not know exactly what he is going to produce but uses whatever he finds around him, whether it be pieces of string, fragments of wood, or old cardboards; in short, it works like a tinkerer who uses everything at his disposal to produce some kind of workable object.”

To say there’s no new genetic information when a new function has evolved (or even when an existing function has improved) is a red herring that is promulgated by the opponents of evolutionary science. In this regard, it seems relevant to point out that the corresponding author, Scott Minnich, is a fellow of the Discovery Institute and was an expert witness for the losing side that wanted to allow the teaching of “intelligent design” as an alternative to evolution in public schools in the landmark Kitzmiller v. Dover case.

“Rapid evolution of citrate utilization”

In the title of their paper and throughout, Van Hofwegen et al. emphasize that, in their experiments, E. coli evolved the ability to grow aerobically on citrate much faster than the 30,000 generations and ~15 years that it took in the LTEE. That’s true, but it also obscures three points. First, we already demonstrated in replay experiments that, in the right genetic background and by plating on minimal-citrate agar, Cit+ mutants sometimes arose in a matter of weeks (Blount et al. 2008). Second, rapid evolution of citrate utilization—or any evolution of that function—was not a goal of the LTEE. So while it is interesting that Van Hofwegen et al. have identified genetic contexts and ecological conditions that accelerate the emergence of citrate utilization (as did Blount et al., 2008), that in no way undermines the slowness and rarity of the evolution of this function in the context of the LTEE (or, for that matter, the rarity of Cit+ E. coli in nature and in the lab prior to our work). Third, the fastest time that Van Hofwegen et al. saw for the Cit+ function to emerge was 19 days (from their Table 1), and in most cases it took a month or two. While that’s a lot faster than 15 years, it’s still much longer than typical “direct selections” used by microbiologists where a readily accessible mutation might confer, for example, resistance to an antibiotic after a day or two.

So while we commend the authors’ patience, we do not think the fact that their experiments produced Cit+ bacteria faster than did the LTEE is particularly important, especially since that was not a goal of the LTEE (and since we also produced them much faster in replay experiments). However, in a manner that again suggests an ulterior nonscientific motive, they try to undermine the LTEE as an exemplar of evolution. The final sentence of their paper reads: “A more accurate, albeit controversial, interpretation of the LTEE is that E. coli’s capacity to evolve is more limited than currently assumed.” Alas, their conclusion makes no logical sense. If under the right circumstances the evolution of citrate utilization is more rapid than it is in the LTEE, then that means that E. coli’s capacity to evolve is more powerful—not more limited—than assumed.

“Speciation Event”

To us, one of the most interesting facets of the evolution of the citrate-using E. coli in the LTEE is its implications for our understanding of the evolutionary processes by which new species arise. Part of the reason for this interest—and the one that’s most easily stated in a popular context—is that the inability to grow on citrate is part of the historical definition for E. coli as a species, going back almost a century. But the deeper interest to us lies not in labeling a new species or debating where to draw the line between species—various criteria are used by different scientists, and inevitably there are many cases that lie in grey areas. Rather, as evolutionary biologists, we are most interested in the process of speciation—the ecological and genetic dynamics that lead to changing biological forms that, over time, are more and more like a new species until, eventually, perhaps far in the future, there is no doubt that a new species has evolved.

In short, speciation is not an event. As Ptacek and Hankison (2009, in Evolution: The First Four Billion Years) put it, “[S]peciation is a series of processes, with a beginning stage of initial divergence, a middle stage wherein species-specific characteristics are refined by various forces of evolution, and an end point at which a new species becomes a completely separate evolutionary lineage on its own trajectory of evolutionary change with the potential for extinction or further diversification into new lineages.” We realize that scientists (ourselves included) often use shorthand and jargon instead of writing more carefully and precisely. We have no doubt that one can find solid scientific papers that talk about speciation events; but except for cases that involve hybridization leading to polyploids that are reproductively isolated in a single generation (as sometimes occurs in plants), this is simply an imprecise shorthand.

In our first paper on the citrate-using E. coli that arose in the LTEE, we clearly emphasized that becoming Cit+ was only a first step on the road to possible speciation (Blount et al., 2008). One criterion that many biologists would apply to investigate speciation is whether a later form merely replaced an earlier form (evolution without speciation) or, alternatively, one lineage split into two lineages that then coexisted (incipient speciation). In fact, we showed that, after the new function evolved, the Cit+ and Cit lineages coexisted (and their coexistence was confirmed using genomic data in Blount et al., 2012). We concluded the 2008 paper by asking explicitly: “Will the Cit+ and Cit– lineages eventually become distinct species?” (emphasis added) and discussing how we might assess their ongoing divergence.

By contrast, Van Hofwegen et al. dismiss the idea of speciation out of hand, not only by calling it an event but by treating the issue as though it hinges, literally, on the individual mutations that produced a Cit+ cell. For example, they write: “[B]ecause this adaptation did not generate any new genetic information … generation of E. coli Cit+ phenotypes in our estimation do not warrant consideration as a speciation event.” And in the penultimate sentence of their paper, they say: “[W]e argue that this is not speciation any more than any other regulatory mutant of E. coli.” (We also note that this is a rather bizarre generalization, as though the gain of function that gave access to a new resource is equal in regards to its speciation potential to, say, the loss of regulation of a function that is no longer used by a lineage in its current environment. Both might well be adaptations, but one seems much more likely to begin the process of speciation.)

In conclusion, Van Hofwegen, Hovde, and Minnich have done some interesting experiments that shed further light on the nature of the mutations and ecological conditions that allow E. coli cells to evolve the ability to grow aerobically on citrate, a function that this species cannot ordinarily perform. However, they misunderstand and/or misrepresent the relevance of this system for evolutionary biology in several important respects. 

And the meaning of historical contingency

The paper by Hofwegen et al. is accompanied by a commentary by John Roth and Sophie Maisnier-Patin. Their abstract begins: “Van Hofwegen et al. demonstrate that E. coli rapidly evolves ability to use citrate when long selective periods are provided. This contrasts with the extreme delay (15 years of daily transfers) seen in the long-term evolution experiments of Lenski and coworkers. Their idea of ‘historical contingency’ may require reinterpretation.”

Historical contingency is a complicated notion, but it essentially means that history matters. In Blount et al. (2008), we made it clear what we mean by historical contingency in the context of the evolution of the Cit+ lineage in one of the LTEE populations. Was this an extremely rare event that could have happened at any time? Or did it instead depend on the occurrence of a sequence of events, a particular history, whereby an altered genetic context evolved—a potentiated background—in which this new function could now evolve?

Roth and Maisnier-Patin’s suggestion that our idea of “historical contingency” may require reinterpretation reflects a false dichotomy between historical contingency, on the one hand, and the effects of different selection schemes, on the other. The fact that evolution might be fast and not contingent on genetic background (though the evidence of Van Hofwegen et al. is, at best, ambiguous in this regard) in one set of circumstances has no bearing on whether it is contingent in another set of circumstances. The historical contingency of Cit+ evolution is not mere conjecture. We showed that the evolution of this new function in the LTEE was contingent. In replay experiments, Blount et al. (2008) showed that that the Cit+ trait arises more often in later-generation genetic backgrounds than in the ancestor or early-generation backgrounds. Moreover, Blount et al. (2012) performed genetic manipulations and showed that a high-copy-number plasmid carrying the evolved module that confers the Cit+ function had very different phenotypic effects when put in a Cit clone from the lineage within which Cit+ evolved than when placed in the ancestor or even other late-generation lineages not on the line of descent leading to the emergence of the Cit+ bacteria. In the clone on the line of descent, this module conferred strong, immediate, and consistent growth on citrate. In the other genetic backgrounds, growth on citrate was weak, delayed, and/or inconsistent.

The hypothesis of historical contingency is not mutually exclusive with respect to causal factors of an ecological or genetic nature—it simply says that factors that changed over time were important for the eventual emergence of Cit+. Moreover, historical contingency was invoked and demonstrated in a specific context, namely that of the emergence of Cit+ in the LTEE—it does not mean that the emergence of Cit+ is historically contingent in other experimental contexts, nor for that matter that other changes in the LTEE are historically contingent—in fact, some other evolved changes in the LTEE have been highly predictable and not (or at least not obviously) contingent on prior mutations in the populations (e.g., Woods et al., PNAS, 2006). [For more on historical contingency and the LTEE, you can download a preprint of Zack’s latest paper from his website: Blount, Z. D. A Case Study in Evolutionary Contingency. Studies in the History and Philosophy of Biology and Biomedical Sciences.]

Erik Quandt offers this analogy to illustrate our point that contingency depends on context: “It’s kind of like the difference between being an average person attempting to dunk a basketball when all by yourself, with unlimited time, and maybe even with a trampoline versus having to get to the rim in a game with LeBron James and the Cavs playing defense. Just because you can do it by yourself under optimal conditions, does this negate the difficulty of doing it in an NBA game or say anything about the kind of history (training and/or genetics) that you would need for that situation?”

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LTEE lines centered on citrate #11

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An Absence of Posts, an Abundance of Talks, and More

Dear Reader:  No, I have not given up on this blog.  But I’ve been busy, busy, busy!

In the last four weeks alone, I have traveled to the University of Arizona, Harvard University, Duquesne University, and Princeton University.  Besides giving talks at each place (two public lectures and two academic seminars, with cumulative audiences of well over a thousand people), I have met with dozens and dozens of amazing scientists, from graduate students and postdocs to faculty both young and old.  It’s been a blast:  an exhausting blast, but a blast all the same!

And next week?  I’m hosting four terrific colleagues from two continents who will work with me to begin making sense of hundreds of newly sequenced genomes from the LTEE.

Oh, and we have some more job searches starting next week.

And did I mention?  We just had a fascinating (if I may so myself) and complex paper come out today in Science (on-line express for now) on the most deeply divergent (i.e., oldest sustained polymorphism) of the 12 LTEE populations.  And no, it’s not about the citrate eaters from population Ara–3.

Plucain, J., T. Hindré, M. Le Gac, O. Tenaillon, S. Cruveiller, C. Médigue, N. Leiby, W. R. Harcombe, C. J. Marx, R. E. Lenski, D. Schneider.  2014.  Epistasis and allele specificity in the emergence of a stable polymorphism in Escherichia coli.  Science.

It’s population Ara–2 instead, where two lineages—dubbed the Larges (L) and Smalls (S)—have coexisted for several tens of thousands of generations.  In superb research led by Dr. Jessica Plucain that she did in the lab of my long-time collaborator (and dear friend!) Prof. Dom Schneider (Grenoble, France), Jessica led the work to identify—out of hundreds of mutations—three that are sufficient to allow a “constructed” S ecotype (i.e., the ancestor plus three derived alleles) to invade and stably coexist with the evolved L ecotype.  Ecological context and specific genetic interactions are key to establishing this “half” of the polymorphism … and the other “half” of the story— what makes the L ecotype special—might well turn out to be just as complex, or perhaps even more so.

The S and L types are especially challenging (even painful!) to work with because this population became a mutator very early on—before the two lineages diverged—and so there are many, many mutations to contend with; moreover, they make colonies on agar plates that are quite challenging to score and count.  So congratulations to Jessica, Dom, and other members of Dom’s lab for their perseverance in studying this extremely interesting population.

Also on the list of authors are Prof. Chris Marx and two members of his lab.  They performed metabolic analyses showing how the carbon fluxes through the central metabolism of the S ecotype have diverged from both the ancestor and the L ecotype.  Chris was a postdoc in my lab almost a decade ago, but most of his work (then and since) has been on experimental evolution using Methylobacterium, and so this is the first paper we’ve co-authored.

There was a production error, though, in the on-line version of our paper; the final sentence of the abstract was dropped (except for one word).  The abstract, in total, should read as follows:

“Ecological opportunities promote population divergence into coexisting lineages. However, the genetic mechanisms that enable new lineages to exploit these opportunities are poorly understood except in cases of single mutations. We examined how two Escherichia coli lineages diverged from their common ancestor at the outset of a long-term coexistence. By sequencing genomes and reconstructing the genetic history of one lineage, we showed that three mutations together were sufficient to produce the frequency-dependent fitness effects that allowed this lineage to invade and stably coexist with the other. These mutations all affected regulatory genes and collectively caused substantial metabolic changes. Moreover, the particular derived alleles were critical for the initial divergence and invasion, indicating that the establishment of this polymorphism depended on specific epistatic interactions.”

[Edited on 07-Mar-2014:  The on-line PDF at Science Express now has the complete abstract.]

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The picture below shows Dom Schneider and Richard Lenski in Paris in 2013.  They are holding a petri dish that Jessica Plucain made to celebrate the 25th birthday of the LTEE.

Dom and Rich, Paris, 2013

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