It could have been career suicide for scientists to voice suspicions about a possible lab leak, says Metzl, especially when there was already a long history of viral disease outbreaks spilling over from nature. Alina Chan, a postdoctoral fellow specializing in gene therapy and cell engineering at the Broad Institute in Cambridge, Massachusetts, echoes that view. Chan says the risk of challenging the orthodoxy that SARS-CoV-2 has natural origins—an entirely plausible hypothesis, she maintains—is greatest for established scientists in infectious disease with supervisory roles and staffs to support. She herself has spent much of the last year calling for more scrutiny of a potential lab leak, claiming that as a postdoc, she has less to lose.
The vitriol also obscures a broader imperative, Relman says, which is that uncovering the virus’s origins is crucial to stopping the next pandemic. Threats from both lab accidents and natural spillovers are growing simultaneously as humans move steadily into wild places and new biosafety labs grow in number around the world. “This is why the origins question is so important,” Relman says.
“We need a much better sense about where to place our resources and effort,” he adds. And if a lab release for SARS-CoV-2 looks plausible, Relman says, “then it absolutely deserves a whole lot more attention.”
If SARS-CoV-2 did spill over into humans from the wild, how and where did that happen? A year into the pandemic, these remain open questions. Scientists still speculate about whether the virus passed directly into humans from infected bats (known reservoirs for hundreds of different coronaviruses) or through an intermediary animal species. The Huanan Seafood Wholesale Market in Wuhan was initially thought to be the originating site of a potential spillover, since that’s where the first cluster of covid-19—the disease caused by the virus—was detected. But newer evidence suggests that animal or human infections may have been circulating elsewhere for months beforehand, and the focus has since broadened to other markets in the city, wildlife farms in southern China, and other possible scenarios, such as consuming virally contaminated frozen meat originating in other provinces.
Importantly, the virus’s immediate ancestors have yet to be identified. The closest known relative, a coronavirus dubbed RaTG13, is genetically 96% similar to SARS-CoV-2.
A lab-escaped virus, meanwhile, would have been introduced to the world by a researcher or technician who became infected with it. These sorts of lab leaks have happened before, and were implicated in several cases of community transmission during SARS outbreaks in the early 2000s. In 2017, the Wuhan Institute of Virology became the first lab in mainland China to receive a Biosafety Level 4 (BSL-4) designation, the highest security status for a research space. But the institute also has a history of questionable safety practices. The lab’s scientists reported a lack of appropriately trained technicians and investigators at the facility, prompting US diplomatic scientists who visited in 2017 and 2018 to alert the State Department. At the same time, many scientists have pointed out, particularly in the aftermath of a recent, and for some, contentious, examination of the lab-leak hypothesis in New York magazine, that coronaviruses have typically been handled at BSL-2 or BSL-3—lower security levels.
Such caveats aside, a prevailing theory among lab-leak proponents has been that SARS-CoV-2 was not simply brought into the Wuhan lab but was somehow engineered there, given that many of its scientists routinely perform genetic research on coronaviruses and may also have “collaborated on publications and secret projects with China’s military,” according to a US State Department fact sheet released during the last week of the Trump administration. On March 9, a Washington Post columnist, citing an unnamed State Department official, suggested that the Biden administration—while stopping well short of endorsing any particular theory regarding the origin of the virus—did not dispute many of the points made in that fact sheet.
Still, skeptics who doubt the lab-leak hypothesis say SARS-CoV-2 doesn’t look anything like an engineered virus. Instead of appearing in discrete chunks, as would be expected with a genetically engineered microbe, the differences with RaTg13 are distributed randomly throughout the viral genome. In an email to Undark, University of Chicago emeritus virology professor Bernard Roizman wrote that “we are many, many years away from a complete understanding of viral gene functions and regulation—the key elements critical for construction of lethal viruses.”
The virus does have an inexplicable feature: a so-called “furin cleavage site” in the spike protein that helps SARS-CoV-2 pry its way into human cells. While such sites are present in some coronaviruses, they haven’t been found in any of SARS-CoV-2’s closest known relatives. “We don’t know where the furin site came from,” says Susan Weiss, a microbiologist who co-directs the Penn Center for Research on Coronaviruses and Other Emerging Pathogens at the University of Pennsylvania’s Perelman School of Medicine. “It’s a mystery.” Although Weiss says SARS-CoV-2 is unlikely to have been engineered, she adds that the possibility that it escaped from a lab can’t be ruled out.
Relman says it’s also possible that scientists working with undisclosed and even more closely related coronaviruses—perhaps one with a furin cleavage site and another with the SARS-CoV-2 gene backbone—may have been tempted to create a recombinant virus so they could study its properties. Indeed, researchers at the Wuhan Institute of Virology initially failed to disclose that eight other SARS-like coronaviruses had been detected in samples collected from the same mine cave where RaTG13 was found. Workers who cleaned bat feces in that cave, located in Yunnan Province near the border with Laos, went on to develop severe respiratory disease, and one of them died.
Petrovsky leans towards another potential scenario, namely that SARS-CoV-2 might be evolved from coronaviruses that snuck into lab cultures. Related viruses in the same culture, he explains, such as one optimized for human ACE2 binding and another not, can swap genetic material to create new strains. “We’ve had this sort of thing happen in our own lab,” he says. “One day, you’re culturing flu, and then one day you sequence it, and you go, ‘Holy shit, where did this other virus come from in our culture?’ Viruses are evolving the whole time, and it’s easy for a virus to get into your culture without you knowing it.” Petrovsky and several coauthors speculated in a paper published as a non-peer-reviewed preprint in May of last year as to whether the virus was “completely natural” or whether it originated with “a recombination event that occurred inadvertently or intentionally in a laboratory handling coronaviruses.” The team wasn’t “saying this is a lab virus,” Petrovsky emphasizes, but rather “just presenting our data.”
But in late April 2020, as Petrovsky’s group was thinking about where to publish their work, “Trump blurted out” that he had reason to believe the virus came out of a Chinese lab, Petrovsky says. And at that point, he adds, much of “the left-wing media” decided “they were going to paint the whole lab thing as a conspiracy theory to bring down Trump.” When Petrovsky approached administrators of the preprint server bioRxiv, the paper was refused. BioRxiv staff replied that it would be more appropriately distributed after peer review, “which stunned us,” Petrovksy says. “We thought the whole point of preprint was to get important information out quickly.”
The paper was subsequently posted on a different preprint server called arXiv.org, based out of Cornell University. Soon reporters came calling, but most were from right-wing news outlets representing what Petrovsky calls “the Murdoch press.” Petrovsky says he had to work at stopping some tendentious reporters from distorting his paper’s findings to shape a narrative that SARS-CoV-2 had unequivocally been manufactured. And at the same time, he says, other media tried “to make a mockery of the whole possibility of the lab thing.”
The Blue Technology Barometer 2022/23
The overall rankings tab shows the performance of the examined
economies relative to each other and aggregates scores generated
across the following four pillars: ocean environment, marine activity,
technology innovation, and policy and regulation.
This pillar ranks each country according to its levels of
marine water contamination, its plastic recycling efforts, the
CO2 emissions of its marine activities (relative to the size
of its economy), and the recent change of total emissions.
This pillar ranks each country on the sustainability of its
marine activities, including shipping, fishing, and protected
This pillar ranks each country on its contribution to ocean
sustainable technology research and development, including
expenditure, patents, and startups.
This pillar ranks each country on its stance on ocean
sustainability-related policy and regulation, including
national-level policies, taxes, fees, and subsidies, and the
implementation of international marine law.
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Methodology: The Blue Technology Barometer 2022/23
Now in its second year, the Blue Technology Barometer assesses and ranks how each of the world’s largest
maritime economies promotes and develops blue (marine-centered) technologies that help reverse the impact of
climate change on ocean ecosystems, and how they leverage ocean-based resources to reduce greenhouse gases and
other effects of climate change.
To build the index, MIT Technology Review Insights compiled 20 quantitative and qualitative data indicators
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international ocean sustainability organizations. Through trend analysis, research, and a consultative
peer-review process with several subject matter experts, weighting assumptions were assigned to determine the
relative importance of each indicator’s influence on a country’s blue technology leadership.
These indicators measure how each country or territory’s economic and maritime industries have affected its
marine environment and how quickly they have developed and deployed technologies that help improve ocean
health outcomes. Policy and regulatory adherence factors were considered, particularly the observance of
international treaties on fishing and marine protection laws.
The indicators are organized into four pillars, which evaluate metrics around a sustainability theme. Each
indicator is scored from 1 to 10 (10 being the best performance) and is weighted for its contribution to its
respective pillar. Each pillar is weighted to determine its importance in the overall score. As these research
efforts center on countries developing blue technology to promote ocean health, the technology pillar is
ranked highest, at 50% of the overall score.
The four pillars of the Blue Technology Barometer are:
Carbon emissions resulting from maritime activities and their relative growth. Metrics in this pillar also
assess each country’s efforts to mitigate ocean pollution and enhance ocean ecosystem health.
Efforts to promote sustainable fishing activities and increase and maintain marine protected areas.
Progress in fostering the development of sustainable ocean technologies across several relevant fields:
- Clean innovation scores from MIT Technology Review Insights’ Green Future Index 2022.
- A tally of maritime-relevant patents and technology startups.
- An assessment of each economy’s use of technologies and tech-enabled processes that facilitate ocean
Commitment to signing and enforcing international treaties to promote ocean sustainability and enforce
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What Shanghai protesters want and fear
You may have seen that nearly three years after the pandemic started, protests have erupted across the country. In Beijing, Shanghai, Urumqi, Guangzhou, Wuhan, Chengdu, and more cities and towns, hundreds of people have taken to the streets to mourn the lives lost in an apartment fire in Urumqi and to demand that the government roll back its strict pandemic policies, which many blame for trapping those who died.
It’s remarkable. It’s likely the largest grassroots protest in China in decades, and it’s happening at a time when the Chinese government is better than ever at monitoring and suppressing dissent.
Videos of these protests have been shared in real time on social media—on both Chinese and American platforms, even though the latter are technically blocked in the country—and they have quickly become international front-page news. However, discussions among foreigners have too often reduced the protests to the most sensational clips, particularly ones in which protesters directly criticize President Xi Jinping or the ruling party.
The reality is more complicated. As in any spontaneous protest, different people want different things. Some only want to abolish the zero-covid policies, while others have made direct calls for freedom of speech or a change of leadership.
I talked to two Shanghai residents who attended the protests to understand what they experienced firsthand, why they went, and what’s making them anxious about the thought of going again. Both have requested we use only their surnames, to avoid political retribution.
Zhang, who went to the first protest in Shanghai after midnight on Saturday, told me he was motivated by a desire to let people know his discontent. “Not everyone can silently suffer from your actions,” he told me, referring to government officials. “No. People’s lives have been really rough, and you should reflect on yourself.”
In the hour that he was there, Zhang said, protesters were mostly chanting slogans that stayed close to opposing zero-covid policies—like the now-famous line “Say no to covid tests, yes to food. No to lockdowns, yes to freedom,” which came from a protest by one Chinese citizen, Peng Lifa, right before China’s heavily guarded party congress meeting last month.
While Peng hasn’t been seen in public since, his slogans have been heard and seen everywhere in China over the past week. Relaxing China’s strict pandemic control measures, which often don’t reflect a scientific understanding of the virus, is the most essential—and most agreed-upon—demand.
Biotech labs are using AI inspired by DALL-E to invent new drugs
Today, two labs separately announced programs that use diffusion models to generate designs for novel proteins with more precision than ever before. Generate Biomedicines, a Boston-based startup, revealed a program called Chroma, which the company describes as the “DALL-E 2 of biology.”
At the same time, a team at the University of Washington led by biologist David Baker has built a similar program called RoseTTAFold Diffusion. In a preprint paper posted online today, Baker and his colleagues show that their model can generate precise designs for novel proteins that can then be brought to life in the lab. “We’re generating proteins with really no similarity to existing ones,” says Brian Trippe, one of the co-developers of RoseTTAFold.
These protein generators can be directed to produce designs for proteins with specific properties, such as shape or size or function. In effect, this makes it possible to come up with new proteins to do particular jobs on demand. Researchers hope that this will eventually lead to the development of new and more effective drugs. “We can discover in minutes what took evolution millions of years,” says Gevorg Grigoryan, CEO of Generate Biomedicines.
“What is notable about this work is the generation of proteins according to desired constraints,” says Ava Amini, a biophysicist at Microsoft Research in Cambridge, Massachusetts.
Proteins are the fundamental building blocks of living systems. In animals, they digest food, contract muscles, detect light, drive the immune system, and so much more. When people get sick, proteins play a part.
Proteins are thus prime targets for drugs. And many of today’s newest drugs are protein based themselves. “Nature uses proteins for essentially everything,” says Grigoryan. “The promise that offers for therapeutic interventions is really immense.”
But drug designers currently have to draw on an ingredient list made up of natural proteins. The goal of protein generation is to extend that list with a nearly infinite pool of computer-designed ones.
Computational techniques for designing proteins are not new. But previous approaches have been slow and not great at designing large proteins or protein complexes—molecular machines made up of multiple proteins coupled together. And such proteins are often crucial for treating diseases.