The U.S. Food and Drug Administration approved Moderna’s new XFG-formulated COVID-19 vaccines on August 27, 81 days before a human study specifically designed to evaluate the safety and immune response of the new formulations was scheduled to begin.

Pfizer’s XFG COVID vaccine formulation was approved on the same day, equally without safety data for its new formulation.

FDA approved the 2026–2027 formulas for Moderna’s SPIKEVAX and mNEXSPIKE vaccines, which are claimed to contain genetic instructions corresponding to the alleged JN.1-lineage XFG subvariant.

But FDA’s own Aug 27 approval letters show that a Phase 3b/4 human study specifically evaluating the “Immunogenicity and Safety of MNEXSPIKE and SPIKEVAX 2026-2027 Formula” is not scheduled to begin until November 16.

The first interim results are not due until March 26, 2027.

For younger children eligible to receive the new SPIKEVAX formulation, a separate human safety and immunogenicity study is scheduled to begin even later.

In other words, FDA approved Moderna’s new XFG vaccines first.

The human studies specifically evaluating the new formulations come afterward.

The regulatory pathway allows FDA to rely on claimed evidence from previously licensed formulations rather than require a new human safety trial of every updated formulation before approval.

But that raises consequential health, informed-consent, regulatory, and accountability questions:

• How can FDA determine that a newly reformulated vaccine is safe before formulation-specific human safety data exist?

• What evidence justifies carrying earlier safety findings forward to a changed product?

• And why is FDA requiring a Phase 3b/4 study specifically to evaluate the new formulas’ “Safety” only after Americans are permitted to receive them?

• And most fundamentally, if FDA is approving a newly reformulated vaccine before human safety data specific to that formulation exist, is the agency fulfilling its responsibility to independently establish that products are safe before Americans receive them, or shifting that uncertainty onto the public and collecting the answers afterward?

Congressional committees have confirmed that the FDA “is not meeting important federal safety requirements to protect its employees and the public while also failing to prioritize scientific data quality delivered from FDA laboratories.”

You can contact the FDA here.

And Moderna here.

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FDA Approved the XFG Formula August 27

FDA’s Aug 27 approval letter for SPIKEVAX states:

“We have approved your request received June 5, 2026, to supplement your Biologics License Application (BLA) ... to include the 2026-2027 Formula and associated labeling revisions.”

The agency issued a corresponding approval for the 2026–2027 formula of mNEXSPIKE the same day.

SPIKEVAX is now approved for everyone 65 and older, as well as people ages 6 months through 64 years with at least one underlying condition FDA claims puts them at high risk for severe COVID-19.

mNEXSPIKE is approved for everyone 65 and older and qualifying people ages 12 through 64.

FDA’s prescribing information identifies SPIKEVAX specifically as the “2026-2027 Formula.”

But the approval letters reveal that the prospective human study evaluating the safety and immune response of that formula had not yet begun.

Human Safety Study Starts 81 Days Later

FDA lists the following postmarketing commitment for both Moderna vaccines:

“A Phase 3b/4 Randomized, Open-label Study to Evaluate Immunogenicity and Safety of MNEXSPIKE and SPIKEVAX 2026-2027 Formula”

The study will examine people 65 and older and people ages 12 through 64 with risk factors for what the agency calls severe COVID-19.

FDA gives the following schedule:

• FDA approval: Aug 27, 2026

• Study initiation: Nov 16, 2026

• Study completion: Jan 15, 2027

• Interim results: March 26, 2027

• Final report: June 15, 2027

That means 81 days separate FDA approval from the scheduled beginning of the human study specifically evaluating the safety and immunogenicity of Moderna’s new XFG formulations.

And the company does not have to wait for those results.

Moderna said following the approval that its updated vaccines were expected to become available “in the coming days.”

Children’s Study Starts Even Later

FDA’s SPIKEVAX approval letter identifies another Phase 3b/4 study specifically evaluating the 2026–2027 formulation in children.

The agency describes it as a study:

“to Evaluate the Safety and Immunogenicity of SPIKEVAX 2026-2027 Formula”

The study covers children ages 6 months through 11 years with qualifying risk factors.

It is scheduled to begin Dec 31, 2026.

That is 126 days after FDA approved the formulation.

Yet FDA’s newly approved prescribing information allows SPIKEVAX to be administered to qualifying children beginning at 6 months old.

So for that population, FDA approval precedes the scheduled XFG-specific human study by more than four months.

How Can FDA Get Away With This?

Congress did not pass a law specifically allowing FDA to approve updated vaccines without first obtaining human safety data from the new formulation.

FDA itself created the legally binding framework in 21 CFR § 601.12 under authority delegated by Congress, allowing changes to already licensed biologics to be supported by “clinical and/or nonclinical laboratory studies.”

This raises questions about why the agency responsible for protecting Americans from unsafe products permits newly changed vaccines to reach people before their formulation-specific human safety studies even begin.

Bottom Line

FDA approved Moderna’s new XFG COVID-19 vaccines 81 days before the human study specifically designed to evaluate the new formulas’ alleged safety and immune response was scheduled to begin.

And 126 days before the corresponding study in younger children.

This was not because Congress specifically exempted updated vaccines from new human safety testing.

FDA itself created the regulatory framework that allows changes to licensed biologics to be supported by “clinical and/or nonclinical laboratory studies,” permitting the agency to approve a changed formulation without first obtaining human safety data specific to that formulation.

That puts responsibility for the decision squarely on FDA: The agency charged with determining that biologics are safe approved Moderna’s changed vaccines before the human studies specifically evaluating the safety of those changes had even begun.

Americans are therefore being permitted to receive the new formulations while the very human safety evidence FDA has required for those formulations is still scheduled to be collected afterward.

If FDA can approve a changed vaccine before obtaining human safety data on the change itself, what exactly does an FDA determination of “safe” guarantee to the Americans expected to rely on it?

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The U.S. Food and Drug Administration has approved Pfizer and BioNTech’s new “XFG-adapted” COVID-19 vaccine without the companies first demonstrating in a human clinical trial that the specific XFG formulation is safe, effective, or produces the intended immune response in people.

And the complete nucleotide sequence of the mRNA Pfizer wants injected into people has not been made public by the company, meaning recipients are being asked to consent without knowing either how this specific formulation performs in humans or the exact genetic code the shot is designed to instruct their cells to use to produce the claimed XFG spike protein.

And without disclosure of that sequence, its provenance cannot be independently scrutinized: the public cannot verify exactly where the genetic code came from, how it was derived, what engineered changes were introduced, or whether the sequence being manufactured even matches the genetic instructions Pfizer and FDA claim the shot contains.

Pfizer announced Thursday that FDA approved its supplemental Biologics License Application for COMIRNATY XFG for adults 65 and older and people ages 5 through 64 with at least one underlying condition allegedly placing them at what the company calls “high risk” for severe COVID-19.

Pfizer says the approval was based on a “cumulative body of evidence previously submitted” for COMIRNATY (including clinical, nonclinical, and real-world data) along with “manufacturing/quality and non-clinical data” for the new XFG-adapted formulation.

FDA’s own account confirms the divide: for the 2026–2027 vaccine decision, the agency says it considered human immunogenicity data from existing vaccines but “animal immunogenicity data on new candidate vaccines expressing or containing updated spike components.”

The human evidence belonged to earlier vaccine formulations.

For the newly approved XFG formulation, Pfizer presented nonclinical laboratory antibody measurements.

That raises questions:

• If Americans are going to be injected with the XFG formulation, why was that formulation itself not first tested for safety and efficacy in people?

• What evidence establishes that the human safety profile and clinical benefit of previous formulations can simply be carried forward to a changed shot?

• And how much can the product being injected change before FDA requires the new version itself to undergo human testing?

Congressional committees have confirmed that the FDA “is not meeting important federal safety requirements to protect its employees and the public while also failing to prioritize scientific data quality delivered from FDA laboratories.”

You can contact the FDA here.

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What Is the Exact Genetic Sequence Being Injected?

Pfizer is asking Americans to receive a newly formulated mRNA vaccine without publicly telling them the complete nucleotide sequence of the genetic material being injected.

The company says the newly approved vaccine “target[s] the XFG variant” and describes Pfizer-BioNTech COVID-19 vaccines as based on BioNTech’s “proprietary mRNA technology.”

But naming XFG does not disclose the alleged genetic sequence inside the shot.

Multiple nucleotide sequences are said to be able to encode the same protein.

That means knowing which variant Pfizer says the vaccine targets does not, by itself, reveal the precise sequence Pfizer manufactured, including any engineered nucleotide choices used to produce the encoded spike protein.

Pfizer says FDA reviewed “manufacturing/quality” information for the new XFG formulation.

Yet the complete nucleotide sequence of the XFG vaccine mRNA is not disclosed in Pfizer’s cited XFG supportive-data presentation or its approval announcement.

So people are being asked to consent to receiving alleged genetic material without Pfizer publicly providing the complete genetic sequence they are being asked to receive.

Which raises questions:

• What is the complete nucleotide sequence inside the XFG shot?

• What engineered changes did Pfizer make to it?

• How does it differ from the sequence of circulating XFG and from Pfizer’s previous LP.8.1 formulation?

• Why isn’t the exact sequence publicly disclosed so independent scientists can examine it?

• And how can Americans make a fully informed decision about receiving genetic material when the manufacturer has not publicly disclosed the complete genetic instructions being injected into their bodies?

XFG-Specific Alleged Immune Evidence Came From Mice, Not Humans

Pfizer produced no human clinical immunogenicity trial of the XFG formulation before FDA approved it.

Its May 28 presentation makes the distinction explicit.

The human evidence appears under “LP.8.1 Vaccine Clinical Immunogenicity.”

The new candidate vaccines appear separately under “Preclinical Immunogenicity of Candidate Vaccines.”

Pfizer’s human clinical immunogenicity study involved the previous LP.8.1-adapted vaccine, not XFG.

Pfizer says those human antibody measurements were made with a “validated authentic virus neutralization assay.”

For the new XFG formulation, Pfizer instead presented laboratory antibody measurements from mice.

One slide is titled:

“LP.8.1 and XFG Vaccines Elicit Similar Neutralizing Responses in Naïve Mice; Diminished Against BA.3.2.2.”

The experiment involved just 10 mice per vaccine group, receiving a 0.5-microgram dose, and was said to have measured a 50% neutralization titer using a pseudovirus neutralization assay.

Pfizer also produced similar laboratory antibody measurements in vaccine-experienced mice, again using 10 animals per vaccine group and pseudovirus neutralization.

None of those measurements establishes whether the XFG formulation is safe in humans.

• They do not establish whether it prevents COVID-19 in humans.

• They do not establish whether it reduces hospitalization or death.

• They do not even establish what neutralizing-antibody response the XFG formulation produces in humans.

Pfizer’s own conclusion was narrower:

“LP.8.1 and XFG vaccines exhibited similar patterns of elicited immunity in nonclinical models.”

FDA likewise characterized the XFG evidence as “Nonclinical studies” in its May presentation.

Questions are raised:

• Why should laboratory antibody measurements from groups of 10 mice against pseudoviruses be enough XFG-specific immune-response evidence to proceed to human approval?

• What can those measurements actually establish about safety or disease prevention in a human being?

• And if Pfizer was not required even to demonstrate the new formulation’s intended immune response in people before approval, what XFG-specific evidence could have stopped the shot from reaching the market?

No Human Efficacy Trial of the XFG Shot

Pfizer does present alleged human effectiveness data.

But the people generating those data did not receive the XFG vaccine FDA just approved.

They received the previous LP.8.1 formulation.

Pfizer reports alleged LP.8.1 effectiveness estimates against outpatient and emergency or urgent-care visits and hospitalization.

Pfizer summarizes the evidence this way:

“LP.8.1 vaccine maintained effectiveness during period of XFG predominance.”

That is purported evidence about the previous LP.8.1 shot during a period when XFG was circulating.

It is not an efficacy trial of the XFG shot.

Pfizer does not present a human trial showing that people receiving its new XFG formulation were less likely to become infected, develop COVID-19, be hospitalized, or die.

Nevertheless, the newly approved product is indicated “to protect against coronavirus disease 2019 (COVID-19).”

FDA says its decision was instead based on the “totality of the evidence,” which included alleged current vaccine effectiveness, human immunogenicity evidence from existing vaccines, and animal immunogenicity evidence from the new candidate formulations.

This raises more questions:

• If nobody receiving the XFG shot was studied for clinical efficacy before approval, what direct evidence establishes how much disease this specific shot prevents?

• What is its demonstrated reduction in hospitalization or death?

• How can patients weigh the expected benefit of this formulation when those outcomes were never measured in people receiving it?

No Human Safety Trial of the New XFG Formula

Pfizer does not present a human clinical safety trial of its new XFG formulation.

Instead, Pfizer explicitly says FDA relied on the “cumulative body of evidence previously submitted” for COMIRNATY alongside manufacturing/quality and nonclinical evidence for XFG.

That means previous formulations supply the alleged human safety history being invoked to support the changed formulation.

The distinction is consequential because Pfizer’s own safety information acknowledges that myocarditis and pericarditis have occurred following its mRNA COVID-19 vaccines and says the conditions have occurred most commonly in males ages 12 through 24.

Historical COMIRNATY safety data therefore exist.

But historical safety data from earlier formulations are not observations of what happens to humans after receiving the new XFG formulation.

FDA says it “will continue to monitor the safety and effectiveness of the COVID-19 vaccines.”

That makes the timing of safety evidence an unavoidable question.

• How could an adverse reaction unique to (or occurring at a different rate with) the XFG formulation have been detected before approval when Pfizer did not conduct an XFG human safety trial?

• What evidence establishes that changing the encoded antigen cannot alter the shot’s human safety profile?

• And if an XFG-specific safety signal can only become visible after people start receiving the approved product, how much of the safety question has effectively been pushed from preapproval testing into postmarketing surveillance?

What Would Have Made FDA Say No?

FDA says its XFG recommendation was made “based on the totality of the evidence.”

But FDA’s description of that evidence reveals what the new formulation itself did, and did not, have to demonstrate.

The agency says it considered alleged circulating variants, current vaccine effectiveness, human immunogenicity data from current vaccines, antigenic characterization, human immunogenicity data from the 2025–2026 vaccines, and “animal immunogenicity data on new candidate vaccines.”

FDA’s own May 28 presentation similarly placed its conclusion about XFG under “Nonclinical studies,” claiming that LP.8.1 and XFG vaccines produced cross-neutralizing responses against certain lineages.

Pfizer, meanwhile, acknowledges in its approval announcement that research and development can produce “unfavorable new clinical data” and that regulatory decisions involve determining whether a product’s benefits outweigh its known risks.

But there could be no unfavorable XFG human clinical-trial result before approval if Pfizer was not required to produce such a trial.

There could be no XFG-specific clinical efficacy failure observed in humans if efficacy was not measured in XFG recipients.

And there could be no adverse-event imbalance detected in an XFG clinical trial if no XFG human safety trial was conducted.

More questions are raised:

• What predetermined human safety or efficacy threshold did the new XFG shot have to meet before FDA would approve it?

• What XFG-specific evidence could have revealed a problem serious enough to stop approval?

• If neither human safety nor clinical efficacy had to be demonstrated before approval, exactly what result would have caused FDA to reject the formulation?

Approval First, XFG Human Experience After

Pfizer did not announce that the XFG formulation would undergo human clinical testing before distribution.

It announced immediate shipment.

“This season’s Pfizer and BioNTech COVID-19 vaccine will begin shipping immediately and will be available in pharmacies, hospitals, and clinics across the U.S. in the coming days.”

That means FDA approved the XFG formulation without a human XFG safety trial, human XFG efficacy trial, or human XFG immunogenicity trial, and Pfizer moved directly to distribution.

The first broad human experience with this specific formulation therefore comes after approval, not from a preapproval clinical trial designed to establish its safety or efficacy.

• If an unexpected XFG-specific safety problem emerges after rollout, who is accountable for deciding human testing was unnecessary before approval?

• How many recipients might be exposed before a rare adverse event becomes statistically visible?

• And are patients being clearly told that the specific XFG formulation they are receiving was approved without first being subjected to a human clinical safety or efficacy trial?

Bottom Line

Pfizer’s own evidence draws two critical lines.

First, the company cites human clinical and real-world evidence accumulated from previous COMIRNATY formulations, not human testing of the newly approved XFG shot.

• For XFG itself, Pfizer presents nonclinical laboratory antibody measurements, including experiments involving groups of just 10 mice whose serum was evaluated against pseudoviruses.

• Those measurements did not establish the new formulation’s human safety.

• They did not establish its human efficacy.

• They did not demonstrate that the XFG shot prevents infection, COVID-19, hospitalization, or death in people.

• They did not even establish the XFG formulation’s neutralizing-antibody response in humans.

Second, Pfizer has not publicly provided the complete nucleotide sequence of the mRNA in the new XFG shot in the XFG materials cited here.

• Calling the vaccine “XFG-adapted” does not reveal that sequence.

• Multiple nucleotide sequences can encode the same protein, meaning the public designation does not disclose the precise genetic instructions Pfizer manufactured or what engineered nucleotide choices were incorporated into them.

• Without the complete sequence, independent scientists and recipients cannot independently examine its full provenance: exactly where the genetic code came from, how it was derived, what engineered changes were introduced, how it differs from Pfizer’s previous formulation, or whether the manufactured sequence matches the genetic instructions Pfizer and FDA say the product contains.

FDA nevertheless approved the XFG formulation, and Pfizer announced that shipments would begin immediately.

The result is extraordinary: Americans are being asked to receive a newly changed mRNA formulation that was not subjected to a human safety, efficacy, or immunogenicity trial before approval, while the complete nucleotide sequence of the genetic material they are being asked to receive has not been publicly provided in Pfizer’s cited XFG materials.

FDA calls this an update to an already licensed vaccine.

But a regulatory classification cannot answer the underlying empirical or informed-decision questions.

• What exactly is the complete genetic sequence being injected?

• What is its independently verifiable provenance?

• What human evidence establishes that this specific formulation is safe?

• What human evidence establishes that it prevents disease, hospitalization, or death?

• How can recipients fully scrutinize what they are consenting to when the complete genetic instructions have not been made public?

• And if FDA’s assumptions about either the new formulation’s safety or its similarity to previous shots prove wrong only after rollout, who is accountable?

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The Environmental Protection Agency (EPA) under President Donald Trump is clearing pollution-control barriers around America’s expanding AI infrastructure as researchers report massive nitrogen oxide emissions from an AI power plant and EPA documents identify potentially lethal hazards from newly approved semiconductor chemicals.

The evidence comes from EPA’s own July guidance, an August 25 satellite study, and EPA consent orders reviewed by The Guardian published Wednesday.

It raises immediate health, safety, and environmental questions:

Who will be exposed to the additional pollution and chemicals, what are the cumulative risks to nearby communities, air, and water, and why are regulatory barriers being removed before those risks are fully known?

You can contact the EPA here.

The EPA’s move comes after President Trump announced the $500 billion “Stargate” project in January 2025 to rapidly expand artificial intelligence infrastructure across the United States.

EPA is led by Lee Zeldin, selected by Trump in November 2024 and confirmed by the Senate with a 56–42 vote in January 2025.

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EPA Exempts Certain Data Center Power Plants From Acid Rain Program

On July 27, EPA announced that certain power plants built to supply data centers can operate outside the federal Acid Rain Program if they are disconnected from the public electricity grid.

EPA’s announcement states directly:

“the Clean Air Act (CAA) Acid Rain Program (ARP) does not apply to power generation facilities that are not connected to a public electricity grid.”

EPA calls these “islanded” power generation facilities and specifically says the guidance applies to facilities developed to power data centers.

Why does that matter?

EPA’s own description of the Acid Rain Program says it:

“requires major emission reductions of sulfur dioxide (SO₂) and nitrogen oxides (NOₓ), the primary precursors of acid rain.”

The program also caps sulfur dioxide emissions from covered electric-generating units and requires emissions monitoring.

EPA’s July announcement explains why the off-grid plants escape the program: because they neither sell electricity nor have required reporting to the Department of Energy, EPA concluded they are not subject to it.

If they later connect to the grid, EPA says they may become subject to the program.

In other words, the distinction is regulatory, not physical.

Disconnecting a power plant from the public grid does not mean it cannot emit the pollutants EPA calls the “primary precursors of acid rain.”

Satellite Study Finds NOₓ Emissions 16 Times Expected Level

The pollution concern is not theoretical.

On Aug 23, researchers Kevin Gauld, Daniel Varon, Nicholas Balasus, and Daniel Cusworth submitted a study titled “Quantifying AI data center nitrogen oxide (NOₓ) emissions from space.”

Using NASA’s TEMPO satellite, they measured emissions from the SpaceXAI Colossus 2 power plant in Southaven, Mississippi.

The researchers reported:

“emissions increased through August 2026.”

After February, they estimated average emissions of 730 ± 185 kilograms of NOₓ per hour, roughly 16 times the approximately 47 kilograms per hour they calculated would be expected under the facility’s March 2026 permit for 41 turbines operating under required pollution controls.

NOₓ is one of the pollutants EPA itself calls a “primary precursor” of acid rain.

The study does not establish that EPA’s July decision caused those emissions.

But it provides a real-world warning about the scale of NOₓ pollution that can accompany power generation for massive AI facilities.

EPA Documents Flag ‘Acute Lethality’

Then came another potential hazard.

Yesterday, The Guardian reported that environmental groups had sued EPA over its approval of two photoacid-generator chemicals for semiconductor manufacturing.

The chemicals were approved for import and immediate use in U.S. facilities and appear to be PFAS “forever chemicals,” although The Guardian reported that portions of the EPA documents (including the chemicals’ identities) are redacted.

More importantly, The Guardian reviewed the EPA consent orders themselves.

According to EPA language reproduced in the report, the agency identified potential risks to the public including:

“acute toxicity, genetic toxicity, and systemic, reproductive, and developmental effects”

Potential exposure routes included drinking water, contaminated groundwater, and inhalation from industrial stack releases.

But EPA said:

“The risks for these endpoints were not quantified due to insufficient information on hazard.”

For workers, EPA additionally identified potential respiratory and skin irritation, eye corrosion, and “acute lethality.”

EPA said it had “insufficient data” to assess that acute-lethality risk.

The Guardian rendered “acute lethality” as “sudden death.”

The documents also said EPA could not estimate the environmental hazard because it lacked sufficient information.

Yet the chemicals were approved.

Bottom Line

Three developments now converge around America’s accelerating AI buildout.

• EPA said July 27 that certain off-grid data-center power plants are outside a federal program requiring major reductions in pollutants it calls the “primary precursors of acid rain.”

• Researchers reported Aug 23 that an AI power plant was emitting NOₓ at roughly 16 times the level expected under its permit.

• And The Guardian reported Aug 26, based on EPA consent orders, that EPA approved semiconductor chemicals despite identifying potential hazards including reproductive and developmental effects and “acute lethality” while acknowledging that some health and environmental risks could not be quantified.

Washington wants AI infrastructure built quickly.

But who bears the health and environmental costs of that acceleration?

And why should Americans be exposed to potentially serious hazards before regulators can even quantify the risks?

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The U.S. Department of Health and Human Services (HHS) has funded research that claims to have created 186 new barcoded chimeric “Frankenstein” influenza virus variants, including laboratory-generated viruses that were harder for human antibodies to neutralize than other viruses tested.

The 2026 study, published in Virus Evolution, states:

“After quality control, we had a total of 186 unique barcoded viral variants for the 91 different HAs.”

These were not simply 186 influenza viruses collected from allegedly infected people.

Researchers claim to have built them.

Researchers say they used reverse genetics to build new chimeric viruses by taking a surface protein (hemagglutinin = HA) from modern human flu strains and combining it with the remaining genes of a laboratory-adapted flu virus from 1933.

The paper states:

“The barcoded viruses expressing each different library HA were generated using reverse genetics.”

Researchers then say they “passaged” the newly generated viruses in laboratory cells.

The paper claims that each virus carried a selected contemporary human influenza HA while “all the other genes” came from the lab-adapted 1933 virus.

In plain terms, researchers claim to have created chimeric influenza viruses containing parts associated with different influenza strains.

After COVID-19 demonstrated the potentially catastrophic consequences of a government-led pandemic response, why is HHS still financing the alleged creation of hundreds of new chimeric respiratory viruses, including constructs displaying enhanced immune-evasion phenotypes?

How could such government-funded research be used to trigger, justify, or shape another pandemic response?

What safeguards prevent the resulting sequences, constructs, experimental claims, and technical knowledge from being misused for biological-threat claims, weapons research, surveillance, or another government-led pandemic response?

You can contact NIAID here, NIH here, and HHS here to voice opposition to taxpayer-funded research on pandemic pathogens—particularly after Congress, the White House, the Department of Energy, the FBI, the CIA, and Germany’s Federal Intelligence Service (BND) all acknowledged that the COVID-19 pandemic was “likely” the result of a laboratory incident involving engineered pathogens.

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Some Chimeras Were Said to Be Harder for Human Antibodies to Stop

Researchers say they exposed their newly constructed viruses to human blood serum to determine how effectively human antibodies could neutralize them.

Some of the chimeras were said to be harder to neutralize than others.

For H3N2, the researchers reported:

“subclade K is generally more poorly neutralized by human serum antibodies than other strains”

Some of the K viruses were said to be more resistant still.

Those carrying certain mutations in regions targeted by antibodies produced “especially low titres.”

The paper also claims to have found lower antibody neutralization against H1N1 D.3.1.1 viruses.

Its abstract reports that many human sera had lower titres against both H3N2 K and H1N1 D.3.1.1.

That means the experiment did more than create 186 chimeric influenza viruses.

Researchers created the purported viruses and then identified which of their laboratory-generated chimeras were comparatively better at escaping human antibodies.

The paper says the HA sequences themselves came from naturally occurring influenza strains and that researchers did not add non-natural amino-acid mutations to those HAs.

But the complete chimeric viruses did not come from patients.

Researchers claim to have assembled them by combining those contemporary HAs with the genes of a different, laboratory-adapted influenza virus.

HHS Money Funded the Experimental Work

The federal funding is explicit.

The paper states:

“This work was funded in part by the NIH/NIAID under R01AI165281”

The National Institutes of Health (NIH) and National Institute of Allergy and Infectious Diseases (NIAID) are agencies within HHS.

More importantly, the paper specifies that R01AI165281 supported:

“the experimental work and data analysis performed at the Fred Hutchinson Cancer Center”

The paper lists additional NIH/NIAID funding, including contracts 75N93021C00015 and 75N93021C00014, fellowship F30AI186284, and grant R01AI165818.

HHS was not the study’s only funding source, but federal HHS funding explicitly supported the experimental work.

Why Were 186 Chimeric Flu Viruses Created?

The researchers say the work is intended to measure which influenza variants are least affected by existing human antibodies and help anticipate influenza evolution.

The two groups showing particularly low neutralization (H3N2 K and H1N1 D.3.1.1) subsequently became dominant.

The authors concluded:

“rapid large-scale measurements of neutralization titres provide substantial information relevant to forecasting viral evolution.”

So researchers are not merely watching which alleged influenza variants emerge.

They are building chimeric versions in laboratories and testing which ones can best evade antibodies already present in humans.

That raises a basic question the paper itself does not answer:

Why should federally funded researchers physically create hundreds of new chimeric influenza viruses, including constructs displaying enhanced immune escape, to determine which viral configurations may have an advantage against human immunity?

Bottom Line

HHS funded experimental work in which researchers claim to have created 186 unique barcoded chimeric influenza viruses using reverse genetics.

The viruses combined contemporary human influenza HAs with genes from a lab-adapted 1933 influenza virus and were then grown in laboratory cells.

Researchers subsequently exposed those newly created viruses to human antibodies.

Some were said to be harder to neutralize than others.

The authors described H3N2 K viruses as “more poorly neutralized by human serum antibodies” and reported that some variants produced “especially low titres.”

The central fact is therefore simple:

Federal money supported the creation of 186 new chimeric influenza viruses, and the resulting laboratory virus collection included variants exhibiting enhanced immune escape relative to other pathogens tested.

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A weather-modification company claims its drones sprayed silver iodide into clouds over Alaska and produced roughly 19 million gallons of additional water in about three hours, but its own report shows that water was not directly measured as precipitation that would not otherwise have fallen.

The revelation raises questions about the safety, environmental, and informed-consent implications of private companies pursuing the power to intentionally alter weather, while also raising questions about whether those companies are overstating what their technology can actually do by presenting computer-based, model-derived estimates as demonstrated results.

Rainmaker announced on Sunday that it had:

“successfully validated the impact of drone-based, glaciogenic cloud seeding operations in the Kenai Peninsula, AK on August 22–23, 2026.”

Over roughly three hours, Rainmaker conducted seven coordinated seeding missions.

Two drones released 19 flares containing silver iodide (AgI), a substance used to induce ice formation inside suitable clouds.

“Each flare dispersed approximately 19.7 g of AgI over a nominal 3.5-min seeding period, for a total of approximately 374.3 g of AgI.”

Rainmaker says the intervention worked, claiming the drones “created” precipitation:

“Rainmaker estimates these seeding signatures created 45—65 acre-feet of net new precipitation, with a mean value of 57.6 acre-feet.”

Elsewhere, Rainmaker gives a mean estimate of 57.62 acre-feet, or 71,072 cubic meters (~18.8 million gallons).

But there is a fundamental problem with calling that water “net new.”

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It Was Already Raining

Rainmaker did not seed otherwise identical raining and unseeded clouds and then directly measure the difference.

The clouds were already producing natural precipitation.

Rainmaker acknowledges that its purported cloud-seeding effects appeared within:

“weakening, yet ongoing, natural background precipitation.”

That creates a simple problem.

How can Rainmaker know how much of the subsequent precipitation would have fallen anyway?

It can’t directly observe the answer because there is no second version of the same clouds in which the drones never released silver iodide.

So Rainmaker estimated it.

Because natural precipitation surrounded the purported cloud-seeding effects, researchers selected 5 dBZ to represent the background precipitation already occurring and subtracted it from the radar readings before calculating how much precipitation they attributed to seeding.

Rainmaker does not show how its claimed 19 million gallons would change if researchers selected a different reasonable estimate for the natural precipitation already falling.

They then subtracted that assumed natural background from the radar readings.

“5 dBZ was subtracted from all radar gates prior to conversion to liquid-equivalent precipitation.”

Rainmaker says this subtraction:

“provides an estimate of the precipitation enhancement above the surrounding natural precipitation that would otherwise have occurred in the absence of seeding.”

Read that carefully.

Rainmaker did not observe how much rain “would otherwise have occurred.”

Researchers estimated that amount, subtracted it, and treated the calculated remainder as precipitation attributable to their technology.

That calculated remainder became the basis for the claim that Rainmaker had “created” millions of gallons of “net new precipitation.”

Rainmaker Also Decided Which Radar Signals Counted

There is another layer of human judgment between the drones and the 19-million-gallon claim.

After releasing silver iodide, Rainmaker looked for radar patterns appearing in locations and at times consistent with where researchers expected effects from seeding.

The company reports:

“seven unique seeding signatures were identified on radar utilizing the PAHG NEXRAD.”

Rainmaker defines those signatures as:

“visible patterns on radar which result from cloud seeding operations and are distinguishable from natural background precipitation.”

But those patterns did not arrive labeled CLOUD SEEDING on the radar screen.

Researchers had to attribute them to cloud seeding.

Then:

“each signature is manually tracked using polygons drawn independently for each radar volume and elevation angle.”

In plain English, researchers manually drew boundaries around radar patterns they had already determined were caused by their cloud-seeding operations.

Those boundaries were then used to calculate how much precipitation the purported seeding signatures represented.

Rainmaker itself lists “polygon placement” and “possible contamination from weak natural precipitation” among sources of uncertainty.

Radar Became Gallons Through Mathematical Models

Rainmaker still had not measured 19 million gallons of additional water.

It had radar reflectivity.

To turn those radar signals into an amount of precipitation, Rainmaker used 27 mathematical relationships:

“Reflectivity is converted to liquid-equivalent snowfall rate using 27 relationships.”

The company ultimately calculated:

“the mean precipitation volume attributed to seeding was 57.62 acre-feet (71,072 m³), with a 5th–95th percentile range of 41.70–89.35 acre-feet.”

But even that range does not represent all the uncertainty surrounding the calculation.

Rainmaker explicitly says:

“The ensemble spread reflects uncertainty associated with the choice of Z-S relationship, but does not represent total QPE uncertainty.”

It then identifies additional uncertainty involving radar calibration, beam geometry, what happens to precipitation beneath the radar beam, manually drawn boundaries, and contamination from naturally occurring precipitation.

So the process was not:

Drones flew → 19 million additional gallons were measured.

It was:

Drones flew → radar patterns appeared → researchers attributed patterns to seeding → researchers manually defined their boundaries → researchers assumed how much radar signal represented natural precipitation → that amount was subtracted → the remaining radar signal was converted through mathematical models into an estimated volume of water.

That distinction is enormous.

A Nearly 250-Square-Kilometer Claimed Footprint

Rainmaker nevertheless portrays the experiment as demonstrating control over precipitation across a substantial geographic area.

It says:

“The resulting wet area, defined by accumulated seeded precipitation exceeding 0.01 mm, covered 249.53 km².”

And:

“the core of the affected area received more than 1.0 mm of precipitation attributable to seeding.”

The company also says its system can identify purported effects rapidly:

“This ‘physical validation’ approach enables near real-time identification of seeding signatures and quantitative precipitation estimation (QPE).”

That raises questions extending beyond whether Rainmaker’s 19-million-gallon estimate is correct.

Who Gets to Modify the Weather?

Rainmaker’s report describes drones deliberately dispersing approximately 374 grams (.825 pounds or about 13.2 ounces) of silver iodide into the atmosphere while attempting to increase precipitation across part of Alaska.

If the technology works as Rainmaker claims, obvious public-interest questions follow.

• Who authorized the intervention?

• Who was notified beneath or downwind of the operation?

• What environmental monitoring was performed for the dispersed silver iodide?

• Were residents given any opportunity to consent or object to deliberate modification of precipitation over their communities?

• Who determines where additional precipitation should fall, and who bears responsibility if intentionally altering precipitation produces unintended consequences elsewhere?

And if increasingly capable weather-modification technologies can intentionally influence when and where precipitation falls, the questions eventually extend beyond local environmental policy.

• What prevents the same capability from being used strategically against another region or country?

• How are cross-border atmospheric effects governed?

• At what point does weather modification become a national-security capability?

Rainmaker’s report does not answer those questions.

Nor does the report establish that this particular operation caused environmental or health harm.

Those questions require evidence of exposure, dose, environmental fate, and effects that this validation report does not provide.

But a company deliberately dispersing material into the atmosphere while claiming the ability to alter precipitation makes those questions unavoidable.

The Commercial Claim Deserves Scrutiny, Too

There is another watchdog question.

• How much does Rainmaker’s commercial value depend upon convincing customers, investors, governments, or the public that its technology can reliably manufacture additional precipitation?

That makes the distinction between measured water and model-attributed water especially important.

Rainmaker’s methodology gives researchers consequential discretion at multiple stages of the calculation: identifying which radar features count as seeding signatures, manually defining their boundaries, selecting a value to represent natural precipitation, and converting the remaining radar reflectivity into estimated water.

That does not prove Rainmaker manipulated its analysis, committed fraud, or intentionally inflated its results.

But it creates an obvious question that independent scrutiny could answer:

• Would Rainmaker’s spectacular 19-million-gallon result survive if independent researchers (not the company claiming success) made those analytical decisions?

• What happens if different reasonable natural-background values are used?

• What happens if independent scientists draw the radar boundaries?

• What happens if candidate radar signatures are evaluated without researchers knowing which clouds were seeded?

• And how often does the methodology produce apparent “seeding signatures” in comparable naturally precipitating clouds where no silver iodide was released at all?

Bottom Line

Rainmaker’s experiment establishes something considerably narrower than its headline language suggests.

The company conducted seven drone-based silver iodide releases and subsequently identified seven radar patterns that it says behaved consistently with cloud-seeding effects.

But Rainmaker did not directly observe 19 million gallons of water that would not otherwise have fallen.

The clouds were already raining.

Researchers estimated what portion of the radar signal represented that natural precipitation and subtracted it.

They manually identified and outlined the purported seeding effects.

They converted the remaining radar signal into estimated water using mathematical models.

And Rainmaker acknowledges that even its published uncertainty range does not capture the experiment’s total uncertainty.

Yet the company says its drones “created” “net new precipitation.”

That leaves two problems, not one.

• First, Rainmaker is claiming the ability to deliberately alter precipitation by sending drones into the atmosphere to disperse silver iodide, raising fundamental questions about environmental effects, safety, informed consent, who gets to decide when and where weather should be modified, and what happens when those decisions affect people who never agreed to participate.

• Second, the evidence Rainmaker offers for that extraordinary capability is weaker than the language it uses to sell the result.

The company did not measure 19 million gallons of water that would not otherwise have fallen.

It estimated how much precipitation was natural, subtracted that estimate from radar readings, made additional analytical judgments about which radar signals belonged to its intervention, and converted the resulting signals into an estimated volume of water.

That distinction matters even more when companies are seeking greater power to deliberately manipulate the environment.

The public is being asked to confront the risks of a technology whose operators claim they can control precipitation while the evidence presented here does not establish that they can do what they say they did.

Rainmaker’s report therefore presents two reasons for scrutiny at once: what the company is attempting to do to the atmosphere and whether it is overstating its ability to do it.

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The Department of Health and Human Services (HHS) is seeking public comment on whether federal vaccine recommendations should operate with “a presumption in favor of individual autonomy, informed consent, and religious freedom,” as the agency reconsiders the framework used to recommend vaccines nationwide.

You can submit a comment here.

The request appears in a Monday Federal Register notice signed by HHS Secretary Robert F. Kennedy Jr. and issued in support of the Task Force on Safer Childhood Vaccines and President Donald Trump’s Aug. 10 executive order on childhood vaccine recommendations.

HHS says it is reconsidering the three categories currently used by the federal government: routine, or “universal,” recommendations; risk-based recommendations; and shared or individual clinical decision-making.

The agency states:

“The Department seeks comment on these categories and whether additional or different categories should be adopted. The Department further seeks comment on the considerations that should be relied upon in setting vaccine recommendations, including the availability and strength of available scientific evidence, the appropriate approach when randomized controlled trial evidence is limited or absent, a presumption in favor of individual autonomy and religious freedom, the downstream legal and programmatic consequences of category assignment, and the communication practices necessary to earn and maintain public trust.”

Under the existing system, HHS says a routine recommendation carries an explicit default:

“Under a routine (universal) recommendation, the default is to vaccinate all persons in an age group absent contraindications.”

By comparison, CDC guidance says that under shared clinical decision-making, “there is no default.”

Public comments are due September 20.

Interested persons are also given the following instructions:

“Interested persons are invited to submit written comments identified by Docket No. HHS-OS-2026-0332 by either of the following methods: (1) Federal eRulemaking Portal: https://www.regulations.gov. Follow the instructions for submitting comments; or (2) Mail: Cynthia Goss, 200 Independence Ave SW, Washington, DC 20201. All submissions received must include the agency name and docket number. Comments received will be posted without change to https://www.regulations.gov, including any personal information provided.”

For further information, requesters are asked to contact:

“Cynthia Goss, Deputy Assistant Secretary for Planning and Evaluation (Health Policy), Performing the Delegable Duties of the Assistant Secretary for Planning and Evaluation, Office of the Secretary, Department of Health and Human Services, (202) 690-7858 or by email at: osaspeinfo@hhs.gov.”

2026 17250

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‘Recommended, But Not During Infancy’

HHS is now asking whether additional categories should replace or supplement that framework—including one explicitly addressing infant vaccination:

“Should additional or different categories be adopted, such as ‘recommended, but not during infancy’ (or otherwise age-de-emphasized recommendations); ‘recommended with qualification’; or ‘shared clinical decision-making with qualification’?”

The agency is also asking whether the schedule should provide greater flexibility for vaccine timing and for “administering vaccines individually versus at a single visit.”

‘Presumption’ for Autonomy, Informed Consent, & Religious Freedom

One of the notice’s most consequential questions concerns what principles should govern federal vaccine recommendations in the first place.

HHS asks:

“What considerations should be relied upon in establishing vaccine recommendations and assigning categories, and under what conditions should each predominate? Commenters are specifically invited to address the availability, quality, and strength of evidence; the appropriate approach where randomized controlled trial evidence is absent, infeasible, or unethical to obtain; disease severity and epidemiology; individual versus population benefit; a presumption in favor of individual autonomy, informed consent, and religious freedom; and feasibility and programmatic consequences.”

HHS immediately follows that question by asking whether uncertainty surrounding vaccine evidence has been effectively hidden inside the recommendation itself:

“When evidence is limited, uncertain, or evolving, how should that uncertainty be reflected in the recommendation itself, whether through category assignment, qualifying language, sunset or re-review provisions, or explicit statements of evidentiary certainty, rather than resolved silently in favor of either a universal recommendation or no recommendation?”

HHS Points to COVID-Era Vaccine Policy

HHS explicitly invokes COVID-19 while explaining why the federal recommendation framework is being reconsidered.

The department says federal COVID vaccine policy “shifted toward progressively broader, population-wide recommendations,” despite criticism over the evidence supporting some of those decisions.

It also recounts the CDC director’s 2021 decision to override an ACIP vote against recommending boosters to certain adults based on occupational or institutional exposure.

HHS writes:

“In the Department’s view, episodes in which the deliberative advisory process reaches a narrower conclusion that is then overridden in favor of a broader, top-down recommendation contribute to public doubt about whether Federal recommendations in fact reflect the process created to produce them, and thereby to mistrust of the system as a whole.”

The department then turns directly to the legacy of COVID-era recommendations:

“The Department’s working premise is that recommendations perceived as premature, overconfident, or insulated from candor about uncertainty, as many Americans perceived certain COVID–19-era recommendations to be, impose lasting costs on the credibility of all Federal vaccine recommendations, including those resting on the strongest evidence.”

HHS separately says a “substantial body of research” indicates that “top-down mandates and coercive measures can sow distrust and provoke psychological reactance,” while acknowledging that other research found mandates increased vaccine uptake.

Bottom Line

HHS has not yet changed federal vaccine recommendations.

The department explicitly says the notice “does not constitute a rule, a proposed rule, or a recommendation,” and does not alter existing vaccine recommendations or coverage requirements.

But the questions now officially before HHS are unusually fundamental:

• whether universal vaccination should remain the default,

• whether some vaccines should be “recommended, but not during infancy,”

• what should happen when randomized controlled trial evidence is absent,

• whether uncertain evidence has been “resolved silently,”

• and whether federal vaccine policy should begin with “a presumption in favor of individual autonomy, informed consent, and religious freedom.”

Interested individuals can submit a comment here.

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The World Health Organization (WHO) says evidence that a person is actually sick is not required for a “laboratory-confirmed” human influenza infection with “the potential to cause a pandemic” to trigger mandatory international reporting.

“Evidence of illness is not required for this report,” WHO states in its latest Influenza at the Human-Animal Interface assessment, which covered July 8 through August 7, 2026.

WHO says countries must “immediately notify WHO of any laboratory-confirmed case of a recent human infection caused by an influenza A virus with the potential to cause a pandemic.”

Then, immediately afterward:

“Evidence of illness is not required for this report.”

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The statement raises an obvious question: If evidence of illness is not required to set off a chain of events that could trigger authoritarian international pandemic response, what evidence is required?

WHO points to a “laboratory-confirmed” finding.

But WHO’s own influenza laboratory manual shows that such determinations are made using real-time RT-PCR tests.

But PCR does not directly observe a virus.

It measures fluorescence (the amount of light emitted) from test chemicals mixed with a sample.

WHO describes PCR methods using fluorescent dyes and probes carrying a fluorescent reporter and quencher.

But fluorescence can also increase through unintended pathways, potentially contributing to a false-positive reading.

Those pathways include reagent cross-reactions, probe cleavage or degradation, reporter or quencher detachment or degradation, optical cross-talk, and changes in reporter–quencher behavior caused by heat—which PCR intentionally applies to the sample over and over during testing.

WHO’s manual confirms that PCR repeatedly heats the test material, including to 95°C, and that real-time PCR results are evaluated by whether fluorescence rises above a threshold.

It also explicitly acknowledges background signal, contamination, and false-positive results.

The implications are difficult to ignore.

WHO is saying evidence of illness is not required at the gateway to its pandemic-potential reporting system, while the “laboratory-confirmed” evidence that can substitute for illness may itself rest on light readings susceptible to false-positive signals.

Bottom Line

The world saw what can follow a pandemic determination during COVID-19: lockdowns, business and school closures, masking and distancing mandates, travel restrictions, and vaccination requirements.

WHO now says “Evidence of illness is not required” for certain “laboratory-confirmed” infections with pandemic potential to trigger international reporting.

If nobody has to be sick, and “laboratory confirmation” can ultimately rest on a fluorescence reading, what evidence of actual disease must exist before governments begin exercising pandemic powers?

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The U.S. Department of State and U.S. Department of Health and Human Services last month revealed they “will immediately release all $600 million in Congressionally appropriated funds” for Gavi, the Vaccine Alliance, Bill Gates’ international vaccine syndicate, according to a State Department press release.

In June 2025, HHS Secretary Robert F. Kennedy Jr. had withdrawn American funding from Gavi, citing child deaths linked to the vaccines the organization dispenses worldwide.

But one year later, Secretary of ​State Marco Rubio promised that the United States would “re-engage” with Gavi, citing a purported Ebola outbreak as justification.

The new State Department press release emphasizes President Donald Trump gave the directive himself.

Per the release:

“Last year, President Trump directed Secretary of Health and Human Services Robert F. Kennedy, Jr. and Secretary of State Marco Rubio to engage directly with Gavi, the Vaccine Alliance (Gavi), and secure meaningful reforms before the United States would consider future support. Those discussions resulted in significant commitments that strengthen vaccine safety, improve public health, and better align Gavi’s work with the principles of Gold Standard Science and transparency. Based on those commitments, the United States will immediately release all $600 million in Congressionally appropriated funds to Gavi for both FY25 and FY26.”

The agreement included “reducing reliance on mercury-containing vaccines, where suitable alternatives are available.”

The release didn’t specify which alternatives would be used nor cite any relevant safety studies.

But it did confirm vaccine manufacturers would benefit:

“Achieving these transitions will require manufacturers to expand production capacity, countries to adopt newer vaccine formulations, and Gavi’s governing Board to complete the necessary approvals. The United States recognizes these implementation challenges but welcomes Gavi’s commitment to work toward these goals through transparent governance and responsible stewardship.”

The Trump admin ultimately wants to “resume its place on the Gavi Board”:

“In addition, given the sizeable contribution the United States is making to improve access to vaccines, the United States expects to resume its place on the Gavi Board. In this role, the United States will continue to hold Gavi accountable for measurable progress toward these commitments. It will evaluate any future U.S. support based on demonstrated performance, accountability, and implementation of these reforms.”

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A former National Institute of Allergy and Infectious Diseases (NIAID) employee, David M. Morens (78), pled guilty in federal court yesterday “to a conspiracy charge stemming from a scheme to evade Freedom of Information Act (FOIA) requests and the Federal Records Act in connection with communications about coronavirus research grants,” according to a press release from the United States Attorney’s Office.

Morens’ guilty plea means the COVID-origins controversy now includes an admitted criminal conspiracy inside the upper ranks of the federal apparatus responsible for orchestrating pandemics.

He faces up to five years in prison, though “actual sentences for federal crimes are typically less than the maximum penalties,” the release chose to emphasize.

Sentencing will be determined by Obama nominee U.S. District Judge Paula Xinis (of the U.S. District Court for the District of Maryland in Greenbelt).

She accepted the guilty plea and scheduled it for November 12, 2026.

Judge Xinis has been characterized as a left-leaning “activist” judge.

Months into the COVID pandemic, Xinis was using federal judicial power to enforce the prevailing government coronavirus framework, ordering expanded testing and medical interventions while explicitly pushing CDC guidelines.

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Per the DOJ’s Tuesday press release:

“David M. Morens, 78, of Chester, Maryland, pled guilty to conspiracy to commit offenses and to defraud the United States. Morens served as a senior advisor in NIAID’s Office of the Director from 2006 through 2022.”

“Kelly O. Hayes, U.S. Attorney for the District of Maryland, announced the guilty plea with Special Agent in Charge Jimmy Paul, FBI Baltimore Field Office, and Special Agent in Charge Marcus L. Sykes, U.S. Department of Health and Human Services Office of Inspector General (HHS-OIG).”

Morens counseled the director (including Anthony Fauci) and senior staff on purportedly emerging infectious disease policy, pandemic orchestration, epidemiological studies, and related scientific matters while helping brief leadership and shape recommendations.

According to Morens, he wasn’t the only one involved in the conspiracy:

“According to his guilty plea, Morens, Co-Conspirator 1, Co-Conspirator 2, and others conspired during the COVID-19 pandemic to defraud the United States after NIH terminated Co-Conspirator 1’s grant. NIH terminated the grant, Understanding the Risk of Bat Coronavirus Emergence, based on allegations that COVID-19 emerged from the Wuhan Institute of Virology (WIV) in Wuhan, China. NIAID awarded the grant to Company 1 and Co-Conspirator 1, who made a subaward to the WIV.”

“Following the termination, Morens and Co-Conspirator 2 pledged to help Co-Conspirator 1 restore the termination of the bat coronavirus grant and counter the narrative that COVID-19 leaked from a lab. In anticipation that their communications would be requested through FOIA Requests, Morens, Co-Conspirator 1, and Co-Conspirator 2 agreed in writing to intentionally hide their communications from public view by corresponding using Morens’s personal Gmail account, rather than his official NIH email account.”

“The co-conspirators used Morens’s personal Gmail account to exchange non-public NIH information; correspond about their efforts to influence NIH to fund Company 1; exchange edits to drafts of letters addressed to NIH leadership for Company 1 and Co-Conspirator 1; and “back-channel” information to Senior NIAID Official 1. According to court documents, each of these matters fell within Morens’s role as senior advisor and constituted federal records that needed to be created, maintained, and exchanged on government systems.”

Morens also admitted to conspiring to accept illegal gratuities, including wine and promised Michelin-starred meals, in connection with authoring a scientific commentary advocating a natural origin for COVID-19:

Additionally, as admitted to in his guilty plea, Morens and Co-Conspirator 1 conspired to pay illegal gratuities. Co-Conspirator 1 gifted Morens wine for his “behind-the-scenes shenanigans,” and arranged for its delivery to Morens’s Maryland residence. Morens then allegedly identified an official act that he could perform to “deserve” the gift, which was to author a scientific commentary in a prominent medical journal advocating that COVID-19 had natural origins. Co-Conspirator 1 suggested he would provide Morens with additional things of value, including meals at Michelin-starred restaurants in Paris, New York, and Washington, D.C.

In May 2024, Morens appeared before the U.S. House of Representatives Select Subcommittee on the Coronavirus Pandemic after a subpoena for his testimony was announced.

You can watch that hearing below:

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The Pentagon’s advanced research agency is seeking technology designed to use artificial intelligence and synthetic biology to physically rewrite proteins inside living systems, reprogram multiple pieces of biological machinery at once, and control physiological functions in real time.

The Defense Advanced Research Projects Agency (DARPA) issued the solicitation for its new Protein ENGineering (PENG) program earlier this month through its Defense Sciences Office.

PENG adds a third capability to the potential protein-control pipeline previously documented in DARPA’s PROSE and NODES programs: PROSE seeks to read “unknown” proteins, NODES seeks to “predict” what they do, and now PENG seeks to physically reprogram them inside living systems—potentially forming a read → predict → reprogram architecture for controlling biology.

But DARPA’s PENG does not merely envision software that models proteins or a new drug targeting one protein.

It wants a general-purpose system capable of finding proteins already operating inside living cells, physically modifying them, and changing what they do.

Including multiple proteins simultaneously.

DARPA calls the desired result “tunable, reversible, and context-dependent control over physiological functions” and says the system should produce what the agency characterizes as “therapeutic outcomes.”

At the highest level, DARPA is attempting to make biological function programmable.

DNA is said to contain biological instructions, but proteins are among the molecular machines that actually carry out those instructions.

Rather than permanently changing the DNA, PENG seeks to intervene directly in that execution layer.

It imagines rewriting proteins already functioning inside living systems so physiological responses can be turned up or down in real time.

That capability demands scrutiny beyond DARPA’s stated purposes.

• Could the effort to build a defense against biological threats itself create one? The COVID-19 pandemic has placed a global spotlight on whether pandemic-preparedness and biodefense research may itself have contributed to the emergence of SARS-CoV-2. PENG raises the same national-security problem in advance: Could developing the ability to reprogram biological machinery for claimed “therapeutic” purposes simultaneously create the knowledge and tools required to disrupt that machinery? And could an accident, misuse, or deliberate application turn a purported countermeasure capability into the source of a biological threat?

• Who decides which physiological state is desirable and therefore should be programmed into a living system? If proteins can be manipulated to suppress toxicity, injury, or pathogenesis, could the same machinery be weaponized to disrupt normal physiology or cause harm? How would a person receiving a future PENG-derived intervention independently know which proteins were actually being altered inside his or her body?

• What would meaningful informed consent require for technology capable of simultaneously rewriting multiple pieces of a person’s biological machinery? Could an intervention be misrepresented regarding its targets, duration, off-target effects or physiological consequences? Could favorable biomarkers be produced without the health benefit used to justify the intervention?

• And who ultimately controls the technology? The Pentagon, contractors, pharmaceutical companies, or other commercial licensees? Who profits? Who independently determines whether it is safe? What happens when the computational system designing a biological intervention is wrong? And how could the public independently evaluate those questions if information about the technology can be withheld for national-security reasons?

The PENG solicitation does not establish that these abuses will occur.

But a watchdog assessment of technology this consequential cannot stop with the beneficial purposes asserted by the institution seeking to build it.

It must ask what the capability would make possible if it works.

And whether the safeguards, transparency, and public accountability contemplated by DARPA are commensurate with the power it is attempting to create.

Darpa Ps 26 129

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DARPA Wants to Make Protein Engineering ‘Universal’ & ‘Software-Defined’

DARPA describes PENG as something considerably broader than another biomedical research project:

“The Defense Advanced Research Projects Agency (DARPA) is soliciting innovative proposals for the development of a multifunctional, target-agnostic platform for precise, programmable editing of endogenous proteins under the Protein ENGineering (PENG) program. The PENG program seeks to transform proteome engineering from a collection of isolated, bespoke tools into a universal, software-defined discipline.”

“Target-agnostic” is important.

DARPA is not asking contractors to develop one intervention against one predetermined protein.

It wants a reusable technological platform that can be programmed for different protein targets.

The distinction moves PENG up an entire level of abstraction: the product DARPA wants is not merely an intervention.

It is the capacity to generate different biological interventions.

Whoever eventually controls such a platform would therefore potentially control not one predetermined biological modification, but machinery designed to be redirected toward different molecular targets.

DARPA Wants to ‘Rewrite Endogenous Proteins’ & Control Physiology ‘On-Demand’

DARPA explains what that platform is supposed to physically accomplish:

“The PENG program will establish a comprehensive, integrated platform composed of modular targeting, generalizable effector chemistries, and multiplexed control systems. Rather than developing isolated, target-specific tools, performers will integrate novel splicing, ligation, and co-translational techniques to directly recognize, modify, and rewrite endogenous proteins within complex biological architectures. Performers may explore multiple technical paths, progressively advancing their approaches from foundational validation to robust demonstrations of efficacy. Ultimately, this integrated platform will be validated in high-fidelity cellular models (e.g., organoids), to provide an integrated proof-of-concept at the tissue level.

“By the end of the 42-month program, DARPA expects performers to deliver a fully integrated editing prototype platform capable of executing multiplexed, on-demand modifications to endogenous proteins. The culminating prototype deliverable will be a validated system that provides tunable, reversible, and context-dependent control over physiological functions, achieving transient, mechanism-based therapeutic outcomes without requiring permanent genomic modifications or relying on pharmacological approaches. The prototype platform should demonstrate robust adaptability and rapid programmability against diverse, previously unannounced protein targets and achieve multiple point-specific, measurable and functional outcomes in complex tissue models and animal models.”

The ontology here is important.

Existing proteins inside living biological systems would be physically changed.

Those altered proteins would function differently.

DARPA wants multiple such modifications to occur on demand so that physiological functions themselves can be controlled.

DARPA further requires “rapid programmability” against targets that have not even been announced beforehand.

That raises a different question from whether one experimental treatment works:

• What ultimately limits what biological function a target-agnostic system could be programmed to alter?

• And if DARPA succeeds in producing a reusable platform rather than a single intervention, what safeguards travel with the technology when it is repurposed for a different protein, physiological function, or customer?

Turning Physiological Responses Up or Down in ‘Real-Time’

DARPA contrasts PENG with purported genetic engineering:

“The defense and broader scientific research communities require agile biological technology to constantly shift and adapt for changing environments and needs. Currently, advanced interventions rely on indirect pharmacology or permanent genomic editing (e.g., CRISPR). While powerful, gene editing acts as a permanent, binary switch. Modifying a gene to survive an acute insult leaves lasting changes, potentially causing permanent physiological alterations or unintended off-target effects.”

Instead of changing those purported underlying genetic instructions, DARPA wants access to what it calls the “functional execution layer”:

“To achieve dynamic biological resilience, the ideal intervention point is the functional execution layer: the mature protein. Direct protein editing provides a non-permanent and non-heritable tunable control knob. It enables transient modification of a protein’s structure, activity, or localization in real-time, allowing physiological responses to scale up or down just long enough to limit acute cellular stress, toxicity, injury, or pathogenesis. Proteins may then naturally degrade and turn over, or their half-life dynamics can be explicitly altered through multiplex editing.”

DARPA therefore wants something approaching a programmable control layer between biological instructions and biological outcomes.

The agency characterizes that control as a means of limiting an “acute insult,” toxicity, injury or pathogenesis.

But the underlying capability is broader than the justification: alter protein structure, activity or location → turn physiological responses up or down.

That makes dual use unavoidable as a journalistic question.

• If the same molecular control knob can turn a physiological process down, can it deliberately suppress a necessary function?

• If it can turn one up, can it push that process into a harmful state?

• If the technology can change protein localization, interactions, and cellular machinery, what prevents an authorized therapeutic configuration from being converted into a harmful one?

The document establishes DARPA’s intended application. It does not establish that the underlying capability can only be used for that application.

DARPA Says AI & Synthetic Biology Make It Possible

DARPA says the program is possible because two technological fields are converging:

“A recent convergence of generative AI and synthetic biology has started to overcome these limits. Structural AI models now enable the computational design and in silico modeling of editing machinery onto virtually any mature target with near-atomic precision. Simultaneously, breakthroughs have yielded sequence-tolerant programmable inteins, logic-gated cellular chassis, and co-translational techniques that intercept nascent peptide chains before they fold.”

That moves artificial intelligence beyond observing or classifying biology.

The contemplated chain is:

computationally specify an intervention → design molecular machinery → target existing proteins → physically modify biological machinery → change biological function.

The software’s output can therefore become a physical intervention in a living system.

That raises a basic accountability problem:

• When computational predictions and designs become upstream instructions for manipulating biology, what evidence independently establishes that the computer-designed intervention did what researchers say it did and nothing else?

Wet-lab testing is part of PENG, but the more programmable and target-agnostic the system becomes, the more important independent verification becomes with each new target.

DARPA Wants the ‘Building Blocks of Life’ to Become ‘Reprogrammable’

DARPA itself describes the larger ambition:

“Capitalizing on this inflection point, the PENG program will move beyond isolated tools to establish a universal platform for target-agnostic proteome engineering. Precise control over the proteome will transform biochemistry into a reprogrammable engineering discipline, enabling programmable targeting over the functional building blocks of life. Successful completion of this program will establish a new foundation for advanced research tools for biological interrogation and accelerate the development of tunable, mechanism-based therapeutics, advanced materials, and sustainable solutions.”

That language supplies the larger meaning of the program.

DARPA wants “precise control” over what it calls the “functional building blocks of life” so biochemistry can become “reprogrammable.”

The obvious public-interest question is therefore not simply whether scientists should be allowed to study proteins.

It is who should control a general-purpose technology for programming them.

That question becomes particularly consequential when DARPA itself identifies therapeutics as one destination for the technology, potentially moving programmable biological control from laboratory research toward interventions administered to people.

DARPA Wants Three Capabilities Combined Into One System

DARPA says contractors must integrate three capabilities:

“To realize the vision of transient, programmable control over the proteome, the PENG program seeks to develop a prototype platform that overcomes historic biophysical and biochemical limitations. To achieve this, proposed efforts must integrate three core technological capabilities into a single, unified pipeline:

“Programmable Targeting: Bypassing the historical inability to address specific folded domains by utilizing generative deep learning models and structural computational chemistry tools. This approach enables the computational design of de novo protein binders capable of docking editing machinery to virtually any epitope on a mature, native target protein.

“Generalizable Chemistry: Transitioning away from single use point-solution chemistry toward universal mechanisms for protein modification. Highly innovative approaches may include, but are not limited to:

“Engineering generalized intein chassis capable of logic-gated chemistry triggered by specific cellular environments.

“Utilizing co-translational editing to intercept and modify nascent peptide chains at the ribosomal exit tunnel, bypassing the thermodynamic and steric barriers of fully folded proteins.

“Multiplexing Capability: Deploying multiple, orthogonal, non-interfering editing modules simultaneously. This capability must achieve precise, multi-site reprogramming of biological machines in real-time without destabilizing the mature protein structure or inducing cellular toxicity.”

That last requirement deserves to be read literally.

DARPA wants “multi-site reprogramming of biological machines in real-time.”

Not simulated biological machines.

Not DNA that might eventually produce different proteins.

The biological machinery itself.

And not necessarily one component at a time.

DARPA specifically requires multiplexing.

That makes interactions between simultaneous edits an obvious safety question.

Measuring whether each individual edited protein remains functional does not by itself answer what happens to the larger biological system when several interacting components have been deliberately changed at once.

Capabilities ‘Inaccessible’ to Current Genetic Engineering

DARPA says PENG is explicitly intended to reach beyond what existing genetic engineering can do:

“The program aims to fundamentally transform the way protein function is engineered by shifting the field away from static, genome-level intervention toward direct, reversible, and precise control of the proteome itself. By enabling researchers to target, chemically modify, and simultaneously reprogram multiple sites on existing proteins within living systems, PENG seeks to unlock capabilities that are inaccessible to current genetic engineering approaches, which may include transient correction of protein misfolding or dysfunction, real-time modulation of protein-protein interactions, and dynamic control over cellular machinery without permanent genomic alteration. Success in this program would establish a generalizable, reusable editing platform applicable across diverse protein families and biological contexts, laying the technical foundation for future therapeutic, diagnostic, and biomanufacturing applications built on precise, on-demand proteome engineering.”

This passage completes the abstraction ladder.

DARPA wants to move from manipulating genetic instructions to exercising direct, real-time control over the biological machinery executing those instructions.

And the agency explicitly says success would establish the foundation for future so-called “therapeutic” applications.

PENG itself excludes human-subject research and permits animal research.

But once DARPA identifies future therapeutics as a destination, informed consent becomes a legitimate question before, rather than after, that transition occurs.

If a descendant technology is eventually administered to humans, what exactly must a recipient be told about every intended protein edit, known off-target effect, expected duration, and downstream physiological consequence before consent can reasonably be described as informed?

Bottom Line

DARPA is not merely developing another drug, gene-editing technique, or AI model.

The Pentagon’s research arm is attempting to create a general-purpose interface for programmable biological control: software-assisted machinery capable of physically rewriting proteins already operating inside living systems, modifying several targets simultaneously, and changing physiological functions in real time.

DARPA describes the intended result as “therapeutic outcomes” and says physiological responses could be turned up or down during what it describes as cellular stress, toxicity, injury, or pathogenesis.

But those stated intentions do not define the limits of the capability being created.

If PENG succeeds, the public-interest issue becomes control.

The consequential questions are who gets access to the system, who chooses the proteins it targets, who determines what constitutes desirable physiology, and whether a capability designed to alter biological function can be repurposed to impair it.

If PENG-derived technology eventually becomes a therapeutic product, the issue becomes trust and consent: whether recipients can independently verify what is being altered inside their bodies, whether “temporary” modifications can have lasting consequences, whether risks and off-target effects are completely disclosed, and whether claims about threats, benefits, and safety can be independently tested rather than simply accepted from the institutions developing and selling the intervention.

The issue also becomes power and accountability.

DARPA requires performers to consider commercialization, including business models, IP, licensing, markets, customers and partnerships. At the same time, the agency says some PENG research may disclose capabilities “unique and critical to defense” and anticipates restrictions under which publication of some information or results could require DARPA’s permission.

That combination deserves scrutiny: a Pentagon-funded technology for programmable control over living biology, contemplated for eventual “therapeutic” and commercial use, while some information about what the underlying capability can do may remain restricted for national-security reasons.

The question is not merely whether DARPA can successfully reprogram the “functional building blocks of life.”

It is whether the public will retain the information, independent oversight, and freedom necessary to hold accountable whoever ultimately controls the machine.

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Nearly 14 years before COVID-19, HHS created the Public Health Emergency Medical Countermeasures Enterprise (PHEMCE) and centralized federal health, biomedical, regulatory, homeland-security, and military functions around a government-directed countermeasure pipeline.

Creating what this website refers to as the Pandemic-Industrial Complex (PIC).

PHEMCE centralized seven named federal agencies and components (ASPR, CDC, FDA, NIH, DOD, DHS, and VA) across four Cabinet departments: HHS, DOD, DHS, and VA.

Private industry was explicitly inserted into that structure.

HHS named pharmaceutical manufacturers, biotechnology companies, clinical research organizations, and private research organizations.

Then it proposed increasing industry access to HHS agencies, streamlining regulation, lowering obstacles to private investment, and applying liability protections.

The government’s own documents show the same apparatus stretching from threat detection and intelligence assessments through research, product development, regulation, procurement, stockpiling, deployment, and use.

The national-security conflict is obvious.

What happens when the government institutions helping define an alleged biological threat are centralized with the institutions funding research around it, determining what product should be made, regulating that product, buying it, and organizing its deployment?

And what happens if research financed or overseen within the wider biodefense ecosystem contributes to producing the very threat that activates that apparatus?

Congress, the White House, the Department of Energy, the FBI, the CIA, and Germany’s Federal Intelligence Service (BND) all acknowledged that the COVID-19 pandemic was “likely” the result of a laboratory incident involving engineered pathogens.

HHS itself warned in 2006 that laboratory-engineered organisms:

“might even be mistaken as naturally occurring emerging agents.”

That warning makes the conflict much harder to dismiss.

DARPA’s PROPHECY, ADEPT, P3 and PREEMPT programs provide a concrete example of what this PHEMCE architecture looked like inside DOD: military programs moved from predicting alleged viral evolution to sequence-based pharmaceuticals, compressed countermeasure timelines and animal-virus surveillance, while DEFUSE proposed applying that machinery specifically to SARS-related bat coronaviruses.

Phemce Implplan 041607final

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HHS Creates PHEMCE & Orders Government & Industry to ‘Align & Synchronize’

HHS’s September 8, 2006, draft PHEMCE Strategy states:

“HHS created the Public Health Emergency Medical Countermeasures Enterprise (PHEMCE) in July 2006... The PHEMCE is a coordinated interagency effort led by HHS and charged with the responsibility to: (1) Define and prioritize requirements for public health medical emergency countermeasures; (2) coordinate research, early- and advanced product development and procurement activities to address the requirements; and (3) set deployment and use strategies for medical countermeasures held in the Strategic National Stockpile.”

HHS then defined the reach of the apparatus:

“The PHEMCE Strategy defines the principles and objectives that will guide our Implementation Plan for the entire PHEMCE-surveillance/detection of threats; research, development, acquisition, storage/maintenance, deployment and utilization of medical countermeasures.”

The government justified this concentration by citing alleged CBRN and biological threats, then demanded:

“unprecedented cooperation among all levels of Government, private industry, academia, international partners and the public.”

And under HHS leadership:

“we must align and synchronize efforts on the part of all key stakeholders involved in the PHEMCE.”

HHS also explicitly inserted private industry into the product-development system:

“Private research organizations, pharmaceutical manufacturers, biotechnology companies, and clinical research organizations already have many of the resources and the expertise needed to develop MCM but have been reluctant to make substantial investments in research and development because of market uncertainties.”

Then:

“HHS will work to streamline the regulatory process for medical countermeasures. HHS will facilitate private investment of time, energy and resources in MCM development by removing or lowering obstacles whenever appropriate, including the application of liability protections where appropriate.”

And one passage reveals what HHS itself counted as a “benefit”:

“As with the definition of costs, benefits also go beyond the simple definition of ‘curing disease’ and include concepts such as overall lifecycle of the medical countermeasure including storage, utilization and deployment.”

That is the conflict in plain language.

PHEMCE was not organized solely around whether a product cured disease.

HHS explicitly counted the product’s storage, utilization, and deployment as part of its “benefit.”

The 2007 Plan Turns PHEMCE Into an ‘End-to-End’ Threat-to-Product Pipeline

The April 2007 implementation plan described the centralized structure this way:

“The HHS Public Health Emergency Medical Countermeasures Enterprise (PHEMCE) has taken a holistic, end-to-end approach that considers multiple aspects of the medical countermeasures mission including research, development, acquisition, storage, maintenance, deployment, and guidance for utilization.”

It then names the agencies:

“HHS PHEMCE is a coordinated, intra-agency effort led by the Office of the Assistant Secretary for Preparedness and Response (ASPR) and includes three HHS internal agencies: the Centers for Disease Control and Prevention (CDC), the Food and Drug Administration (FDA), and the National Institutes of Health (NIH). Additionally, HHS PHEMCE collaborates with its ex officio members: the Department of Defense (DOD), the Department of Homeland Security (DHS), the Department of Veterans Affairs (VA) and other interagency stakeholders as appropriate.”

The plan then lays out the threat-to-product sequence.

HHS said medical-countermeasure requirements would incorporate:

“subject matter expert evaluations, domestic and international intelligence information”

and immediately moved to:

“Identify and prioritize near-, mid-, and long-term development and acquisition programs”

for products potentially covering:

“the entire U.S. population.”

Government procurement could then be sized to:

“drive industrial development of the medical countermeasure.”

NIH was ordered to align its research with PHEMCE priorities:

“NIH will align research and development efforts with the PHEMCE priority medical countermeasure programs.”

And HHS wanted:

“a sustainable, continuous stream of promising medical countermeasures in the pipeline that are aligned with top priority HHS PHEMCE requirements for future acquisitions”

The same plan sought technologies permitting:

“rapid identification and characterization of novel threat agents”

followed by:

“rapid production of new vaccines.”

That is the architecture.

Threat designation → intelligence → product requirement → research → industrial development → acquisition → deployment.

Bottom Line

The two HHS documents confirm PHEMCE centralized seven named federal agencies and components across four Cabinet departments while explicitly inserting pharmaceutical and biotechnology interests into the same government-directed countermeasure structure.

HHS ordered participants to “align and synchronize,” proposed streamlined regulation, lower barriers and liability protections for private developers, contemplated government financing through clinical trials, and said government purchases could “drive industrial development.”

NIH was ordered to maintain a “sustainable, continuous stream” of products aligned with “future acquisitions.”

And HHS itself acknowledged that laboratory-engineered organisms could be “mistaken as naturally occurring emerging agents.”

In all, the documents reveal the central conflict of the Pandemic-Industrial Complex: the same broader government system can help define an alleged biological threat, finance research around it, determine the product requirements, integrate private industry, influence regulation, create the market through procurement, purchase the resulting products and organize their deployment.

That conflict is no longer merely hypothetical.

A laboratory incident involving coronavirus research is itself a mainstream hypothesis under consideration for the origin of COVID-19.

If COVID-19 resulted from research connected to the same wider U.S.-funded biodefense and pandemic-preparedness ecosystem represented inside this apparatus, the implications would extend far beyond public health: a government-connected research ecosystem could be implicated in causing an international biological catastrophe while interconnected institutions within that wider system possessed roles in assessing its origin, controlling relevant information and intelligence, and directing the resulting countermeasure response.

Who independently investigates the system when the system itself may be implicated?

And if a government-connected research ecosystem can potentially contribute to producing the biological catastrophe, while the wider apparatus can then define the threat, finance the response, create the product requirements, “drive industrial development” and organize deployment, does the resulting emergency expose the system—or give that same system more money, authority, and power?

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A February 2026 Nature Methods study confirms “the majority of SARS-CoV-2 genomes obtained during the pandemic” were produced using a reconstruction process that can lead to “systematic errors in genomes.”

Those errors did not merely corrupt individual genome records.

According to the authors, “waves of systematic errors” followed waves of COVID variants and had “significant impacts on the inferred phylogenetic tree,” which is the computer reconstruction used to describe how SARS-CoV-2 allegedly evolved and how different samples were genetically related.

The study identifies how this happened: some software simply filled places where sequencing provided no data with letters copied from the SARS-CoV-2 reference sequence.

Because that reference is already the presumed ancestor, “we don’t know what was here” could become “the ancestral sequence was here.”

Those illegitimately computer-supplied letters could then become false evidence that SARS-CoV-2 had mutated back toward its presumed ancestor.

The researchers ultimately went back to the archived sequencing data behind millions of pandemic genomes and rebuilt them.

Afterward, large numbers of supposed evolutionary events disappeared, nearly 30,000 samples changed lineage classification, and one downstream analysis produced 1,400 fewer supposed separate introductions of SARS-CoV-2 into the United States.

The purported biological past had not changed.

The computer-generated genetic record used to reconstruct that past had.

That exposes a problem more fundamental than bad software.

If changing how computers constructed SARS-CoV-2 genomes could change mutations, lineage assignments, and reconstructed transmission history, those things were not simply observations of what the virus did.

They were conclusions drawn from computational reconstructions of sequencing data.

And the reconstructions themselves could be wrong.

The study therefore raises a question that applies far beyond the particular errors Hunt et al. corrected:

Where does the biological evidence end and the computer-generated story of SARS-CoV-2 begin?

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‘The Majority of SARS-CoV-2 Genomes’

Hunt et al. put the scale of the problem in unusually stark terms in the paper’s abstract:

“The majority of SARS-CoV-2 genomes obtained during the pandemic were derived by amplifying overlapping windows of the genome (‘tiled amplicons’), reconstructing their sequences and fitting them together. This leads to systematic errors in genomes unless the software is both aware of the amplicon scheme and of the error modes of amplicon sequencing. Additionally, over time, amplicon schemes need to be updated as new mutations in the virus interfere with the primer binding sites at the end of amplicons. Thus, waves of variants swept the world during the pandemic and were followed by waves of systematic errors in the genomes, which had significant impacts on the inferred phylogenetic tree.”

The passage establishes four facts at once.

1. The majority of pandemic SARS-CoV-2 genomes were reconstructed from amplified, overlapping pieces.

2. The process can lead to “systematic errors in genomes.”

3. The authors say “waves of systematic errors” followed waves of variants.

4. And those errors had “significant impacts” on the purported evolutionary tree.

That matters because the tree is not merely an illustration.

Genetic differences among reconstructed genomes are used to infer (assume) which samples are related, where mutations occurred, which samples belong to which lineages, and how those lineages allegedly descended from earlier ones.

So what?

Systematic errors existed in the evidence before that evidence was converted into evolutionary history.

The fundamental question is therefore unavoidable:

If the majority of SARS-CoV-2 genomes came through a reconstruction process capable of systematic error, and those errors significantly changed the inferred tree, how much of the purported COVID evolutionary history describes actual biology, and how much describes what the reconstruction system software falsely produced?

The Sequencing Data Provided No Answer

Hunt et al. went beneath finished GenBank genomes to examine the archived sequencing reads used to construct them.

In their supplementary analysis, Hunt et al. specifically searched for stretches of GenBank genomes where the archived sequencing reads provided zero coverage.

Meaning there were no sequencing reads showing what genetic letters were actually present there.

Yet the finished GenBank genome claimed definite letters matching the reference sequence.

The researchers said there was “no evidence from the reads to support a call of A, C, G, or T.”

In other words, the underlying sequencing evidence said:

• We don’t know what genetic letters were here.

Yet the finished genome said:

• These specific genetic letters were here.

Whatever confidence one places in genomic sequencing generally, these particular gaps present a simpler problem:

The sequencing data had provided no answer, but the finished genome contained one anyway.

So what?

If no sequencing reads established whether a position contained A, C, G or T, that position could not establish whether one purported virus matched or differed from another there.

Yet genetic matches and differences are precisely what downstream evolutionary analyses use to reconstruct relationships and changes.

If the underlying data did not establish what was there, how could a definite genetic sequence appear in its place and then legitimately become evidence about what SARS-CoV-2 supposedly did?

The Computer Supplied the Presumed Ancestor

Hunt et al. identify how that happened.

Some genome-building software forced segments of the SARS-CoV-2 reference sequence to fill missing data.

The authors call the premise a “false assumption”: when a region contained no reads, software could nevertheless guess that the missing sequence matched the reference.

And the reference represented the presumed ancestral SARS-CoV-2 sequence in the evolutionary analysis.

The operation was therefore:

No sequencing evidence

→ software inserts presumed ancestral sequence

→ finished genome contains definite ancestral letters

→ downstream analysis receives those letters as part of the genome.

The sample did not establish those letters at those positions.

The computer supplied them precisely because there was no evidence.

Hunt et al. say portions of the reference sequence had been used to fill dropouts for “a large number of sequences” and that this effect alone was a “significant cause of reversions in the tree.”

So what?

Researchers were attempting to infer how samples differed from their presumed ancestor while some genome-building software simply inserted that presumed ancestor into the sample wherever evidence was absent.

That reverses the normal direction of evidence.

Instead of the sample independently and directly establishing that it matched the presumed ancestor, the presumed ancestor itself supplied the match.

How can that computer-created similarity then legitimately count as evidence about ancestry or evolution?

Missing Evidence Became False Evolution

The consequences propagated into the evolutionary tree.

The mechanism is simple.

Presumed ancestor:

A

Purported descendant:

G

Later sample with no sequencing evidence:

?

Software supplies reference:

A

The completed genomes now appear to show:

A → G → A

The evolutionary analysis can infer that SARS-CoV-2 mutated away from its presumed ancestor and subsequently mutated back.

But the sample never established the final A.

The computer put it there.

Hunt et al. identify large numbers of reversions as an important signal of artifacts in pandemic evolutionary trees.

So what?

A purported event in SARS-CoV-2’s biological history could originate with information that was never obtained from the relevant portion of the sample.

The epistemic chain had become:

missing evidence → computer-generated sequence → inferred mutation → purported viral history.

If an alleged evolutionary event exists because software first supplied the genetic state requiring that event, what evidence independently establishes that the purported virus itself ever underwent the alleged evolution?

Waves of Variants Produced Waves of Systematic Errors

Hunt et al. describe a problem that evolved alongside the purported virus.

The tiled-amplicon method is said to depend on primers working at particular genetic locations.

According to the authors, new mutations could interfere with those primer-binding sites, requiring the amplification schemes to be updated.

Hence their remarkable statement:

“waves of variants” were followed by “waves of systematic errors in the genomes.”

The error profile therefore was not necessarily constant throughout the pandemic.

It could change as the purported virus changed.

So what?

The same genetic changes researchers were trying to document could alter the performance of the system being used to document them.

That creates an epistemic feedback problem:

Researchers used the sequencing system to identify changing variants, while changing variants could themselves cause changing systematic errors in the sequencing-derived genomes.

When a new genetic pattern appeared during a variant wave, how confidently could downstream researchers distinguish a biological change in SARS-CoV-2 from a new systematic error produced because the purported biological change interfered with the system used to reconstruct its genome?

The Errors Entered the Evolutionary Tree

Hunt et al. make the downstream consequence explicit in their discussion.

They write that:

“the downstream effect of using multiple variable-quality genome assembly workflows, inconsistent QC criteria and the inevitable coevolution of virus and amplicon schemas, led to systematic errors in genomes, and therefore the phylogeny.”

That sentence identifies where the errors went.

Into the genomes.

And therefore:

Into the evolutionary tree.

So what?

Once an erroneous genetic letter enters the finished genome, downstream analysis does not automatically know that the letter represents an illegitimate artifact rather than biology.

It can become another data point from which ancestry and evolution are inferred, not observed; then operationalized.

The authors themselves describe the problem as trying to distinguish “artifact from biology.”

This is big:

If researchers looking at the evolutionary tree had to determine whether something appearing in it represented “artifact” or “biology,” then the tree itself cannot simply be equated with an observed biological history.

Rebuilding the Genomes Erased Supposed Evolution

Hunt and his colleagues went back to the publicly archived sequencing data and rebuilt millions of purported SARS-CoV-2 genomes through a standardized pipeline called Viridian.

Then they compared the resulting evolutionary tree with the GenBank-based tree.

The GenBank tree contained 63 genome positions with at least 200 supposed reversions.

The Viridian tree contained:

20.

The purported viruses had not traveled back through time and changed.

The computational representation changed.

And large numbers of purported evolutionary events disappeared.

So what?

That means those events cannot all be treated as independently established facts about what SARS-CoV-2 biologically did.

At least some depended upon how computers converted sequencing data into finished genomes.

If the underlying biological past is fixed but changing the reconstruction changes the purported evolutionary history, which one is being reported when researchers say “the virus evolved”?

Nearly 30,000 Samples Changed Lineage

The consequences extended to the names assigned to purported viral lineages.

Hunt et al. found 29,475 samples with different Pango coronavirus lineage assignments depending on whether the GenBank genome or Viridian reconstruction was used.

The samples had not changed.

Their computer-generated genetic representation had.

And when that representation changed, nearly 30,000 samples changed classification.

So what?

“This sample was lineage X” sounds like an intrinsic biological fact discovered in the sample.

For these samples, however, the answer depended upon which reconstruction of the sequencing data was submitted to the lineage-classification system.

If a sample’s lineage can change when its computationally reconstructed genome changes, where does biological identity end and algorithmic classification begin?

The Reconstructed History of COVID Entering America Changed

The Nature Methods researchers then tested whether rebuilding the genomes changed something beyond the evolutionary tree: their reconstruction of how many separate times SARS-CoV-2 supposedly entered different countries.

Using the original GenBank genomes, Hunt et al.’s analysis inferred:

• 15,026 separate SARS-CoV-2 introductions into the United States.

Using the genomes Hunt et al. rebuilt from the archived sequencing data:

• 13,626.

The researchers therefore obtained 1,400 fewer inferred U.S. introductions after changing which reconstruction of the sequencing data they analyzed.

Hunt et al. explain that genome errors could separate samples that should have clustered together, causing their geographic analysis to infer additional artificial “introductions.”

Importantly, the team does not attribute all 1,400 specifically to the reference-filling error.

Their rebuilt genomes corrected multiple kinds of errors.

But the epistemic consequence is fundamental.

Neither 15,026 nor 13,626 represents researchers directly observing SARS-CoV-2 enter the United States that many times.

Both are histories inferred from genetic relationships among computationally reconstructed genomes.

Change the genomes, and the inferred history changes by 1,400 supposed introductions.

So what?

Errors originating in the construction of digital genomes were consequential enough to change what the same researchers’ model said had happened in the physical history of the pandemic.

If rebuilding the genetic record can erase 1,400 inferred entries of SARS-CoV-2 into the United States, how many other claims about where the virus supposedly came from, where it went and how it spread depend not on directly observed events, but on the particular computational reconstruction used to infer them?

The Fundamental Problem Is Bigger Than COVID

Hunt et al. frame Viridian partly as a solution for future epidemics and pandemics.

They specifically hope their work will reduce time spent examining trees and trying to distinguish “artifact from biology.”

That language exposes the deeper problem.

The final genetic record can contain both.

And researchers may have to determine afterward which is which.

That matters beyond SARS-CoV-2 because modern genetics routinely moves through layers: biological material produces instrument measurements; software turns measurements into genetic calls; computational pipelines assemble those calls into sequences; algorithms compare those sequences; and models infer relationships, ancestry, and evolutionary history.

Hunt et al. do not establish that genomic sequencing generally is invalid.

They establish something more precise.

And epistemically consequential:

A finished genome is not automatically identical to what the underlying evidence established, and a biological story inferred from that genome inherits errors introduced during its construction.

The distinction becomes critical whenever language collapses those layers.

• A reconstructed sequence becomes “the genome.”

• A difference between reconstructed genomes becomes “a mutation.”

• A computational relationship becomes “ancestry.”

• An inferred tree becomes “evolutionary history.”

At what point in that chain did an instrument-supported observation become a computational inference?

And at what point did the inference begin being reported as though it were the biological event itself?

Bottom Line

The Nature Methods study shows that systematic errors entered SARS-CoV-2 genomes and significantly affected the alleged evolutionary tree built from them.

In one documented failure, software filled regions with the presumed ancestral sequence even though the underlying sequencing reads provided no evidence establishing what was there.

Those computer-supplied letters could then become false evidence of viral evolution.

When Hunt et al. rebuilt millions of genomes from the archived sequencing data, supposed evolutionary events disappeared, nearly 30,000 samples changed lineage classification, and their reconstruction produced 1,400 fewer inferred U.S. introductions.

The purported biological past had not changed; the computational genetic record had, and the history inferred from it changed with it.

The fundamental problem is therefore not merely that some genomes contained errors.

It is that computationally reconstructed genomes were treated as evidence for mutations, lineages, ancestry, evolution, and transmission history even though the study demonstrates that the reconstruction itself could create false biological evidence.

That leaves the central question:

How much of COVID’s purported genetic backstory/alibi was established by the underlying biological evidence, and how much was produced by the computational systems used to turn that evidence into genomes, evolutionary trees and claims about what SARS-CoV-2 supposedly did?

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The Department of Health and Human Services (HHS) is funding research on an experimental vaccine for future H5N1 bird flu pandemic response that combines a chimeric (“Frankenstein”) H5 bird-flu protein with a synthetic copy of an engineered lipid component of Yersinia pestis, the bacterium that causes plague.

The move comes as President Donald Trump recently filled two posts at the top of the government’s pandemic orchestration apparatus, Sara Brenner now leading the White House pandemic office and H5N1-linked Rachel Idowu taking over NSC bioresponse.

Researchers in the new study claim to have tested their vaccine by challenging mice with a second chimera: an engineered H5N1/PR8 influenza virus assembled from parts of two different influenza viruses.

The findings were published last month in the American Society for Microbiology journal mSphere.

The vaccine contains recombinant H5 hemagglutinin (HA) from the H5N1 bird-flu virus A/Vietnam/1203/2004, but the protein is said not simply to be an unchanged copy of the virus’s native H5 protein.

Researchers say they replaced its native transmembrane portion with a heterologous fibritin foldon, creating an H5 protein containing a component from another biological source.

The vaccine’s immune-stimulating ingredient is even more unusual.

Called BECC470s, it is a synthetic copy of an engineered form of lipid A, the biologically active lipid component of bacterial endotoxin.

The molecule’s development traces directly to engineered Yersinia pestis: researchers first produced the BECC470 lipid-A structure using an altered strain of the bacterium that causes plague, before chemically synthesizing BECC470s as a purified version of that structure.

The researchers then challenged vaccinated mice with a chimeric influenza virus said to contain two genome segments from H5N1 bird flu and six segments from another influenza virus, PR8.

All of it is occurring in the context of preparing medical countermeasures against a purported H5N1 virus the researchers characterize as presenting a pandemic threat.

And American taxpayers are paying for it.

The paper identifies funding from an HHS/NIH/NIAID Adjuvant Development Contract and numerous other federal programs.

You can contact NIAID here, NIH here, and HHS here to voice opposition to taxpayer-funded plague component-containing vaccines and research involving pandemic pathogens like influenza—particularly after Congress, the White House, the Department of Energy, the FBI, the CIA, and Germany’s Federal Intelligence Service (BND) all acknowledged that the COVID-19 pandemic was “likely” the result of a laboratory incident involving engineered pathogens.

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The Vaccine Contains a Synthetic Copy of a Component Engineered in Plague Bacteria

BECC470s is the adjuvant in the experimental vaccine, the ingredient intended to activate the immune system.

Physically, BECC470s is an engineered lipid A molecule.

Lipid A is the lipid portion of the outer-membrane lipopolysaccharide, or LPS, of Gram-negative bacteria and is responsible for much of endotoxin’s biological activity.

The history of this particular molecule leads directly to Yersinia pestis.

A 2023 study describing BECC470s says researchers developed the underlying technology through Bacterial Enzymatic Combinatorial Chemistry, or BECC.

According to the researchers, BECC was used to generate novel lipid-A structures in an “attenuated Yersinia pestis (Yp) isolate.”

The researchers identify two versions of the resulting BECC470 molecule:

• BECC470b — the biological version produced from the engineered plague bacterium.

• BECC470s — the synthetic version subsequently made chemically.

The methods leave little ambiguity about the biological version’s physical origin.

Researchers cultured the corresponding Y. pestis KIM6+ strain, collected the bacterial cells, extracted their lipooligosaccharide and then separated out the lipid-A portion.

The authors themselves describe BECC470b as coming from its biological Y. pestis source.

Researchers subsequently chemically recreated that engineered lipid-A structure as BECC470s.

The paper says the synthetic version was produced using proprietary chemical methods and was more than 99% pure.

So the physical-development chain is clear:

Yersinia pestis, the plague bacterium

↓

engineered Y. pestis lipid A

↓

biologically produced BECC470b

↓

synthetic copy, BECC470s

↓

H5N1 bird-flu vaccine

The vaccine does not contain whole Y. pestis bacteria.

The BECC470s put into the vaccine is the chemically manufactured version of the engineered lipid-A molecule, rather than material physically extracted from a current batch of plague bacteria.

But the molecule being copied was developed as an engineered component of Y. pestis.

And it’s added to the drug intentionally to trigger a response from the body.

BECC470s Retains Inflammatory Activity

Researchers claim to have altered the bacterial lipid precisely because lipid A can produce unwanted endotoxin-associated effects.

BECC470s is a hexa-acylated, monophosphorylated lipid-A structure.

So it lacks a 4′ phosphate present in the related bis-phosphorylated BECC438s molecule.

The researchers explain that phosphate groups are important to the endotoxin activity of lipid-A molecules.

But BECC470s was not engineered to eliminate lipid A’s biological activity altogether.

The researchers want it to continue stimulating the immune system.

They specifically tested BECC470 and related molecules for their ability to activate TLR4 and NF-κB, describing this experiment as measuring their ability to “potentially cause inflammation.”

BECC470s activated the pathway.

The researchers found that its NF-κB activation was lower than biologically produced BECC470, but nevertheless concluded that BECC470s continued to function as a TLR4 agonist.

That is the engineering objective: reduce the undesirable toxicity associated with bacterial lipid A while retaining enough of its inflammatory immune-stimulating activity to make it useful in vaccines.

The researchers claim that BECC470s was not toxic in a single-dose rabbit acute-toxicity model conducted by Labcorp.

However, the published paper provides virtually no details about the experiment.

Including the dose tested, number of animals, administration route, observation period, or toxicological endpoints.

Making it impossible from the publication alone to independently assess the scope of that finding.

The Bird-Flu Vaccine Protein Is Chimeric

The plague-derived molecular design is not the vaccine’s only engineered component.

The H5 bird-flu protein used as the vaccine antigen is itself an engineered hybrid, or chimeric, protein.

Researchers claim to have started with H5 hemagglutinin from A/Vietnam/1203/2004 H5N1.

But they did not simply manufacture an unchanged copy of that viral protein.

The study says researchers replaced H5’s native transmembrane domain with a heterologous fibritin foldon trimerization motif.

“Heterologous” means the replacement comes from a different biological source.

The resulting vaccine antigen is said to combine H5 bird-flu protein with a foreign fibritin foldon component.

Put plainly, it is a chimeric H5 vaccine protein: part of the construct is H5 influenza HA, while another part has been replaced with a non-H5 protein element.

Researchers then combined this chimeric H5 protein with 50 micrograms of BECC470s, the synthetic engineered lipid-A adjuvant whose design traces to Y. pestis.

So the experimental vaccine itself brings together:

a chimeric H5 bird-flu protein + a synthetic copy of an engineered plague-bacterium lipid component.

Researchers Used Another Chimera to Test the Vaccine

Researchers then say they tested the vaccine against a second chimera.

The virus used for the principal mouse challenge was not an untouched H5N1 virus.

It was said to be an engineered 6:2 influenza reassortant, meaning its eight genome segments were assembled from two different influenza viruses.

Two segments (the genes encoding H5 and N1) came from A/Vietnam/1203/2004 H5N1.

The remaining six genome segments came from A/Puerto Rico/8/1934, commonly called PR8.

In plain English, researchers created an H5N1/PR8 influenza chimera carrying the purported H5 and N1 surface components of bird flu on the six-segment internal genetic backbone of a different influenza virus.

Researchers made another alteration to the H5 component: they removed its polybasic cleavage site.

The resulting HALo configuration was designed to be low-pathogenic.

So this study does not document scientists making H5N1 more pathogenic.

It documents researchers using an engineered, attenuated influenza chimera to test an experimental vaccine made with plague components.

The paper credits Mount Sinai researcher Weina Sun with supplying the PR8/H5 virus used in the study.

The experiment therefore contains two separate alleged chimeras:

• Chimera No. 1 — the vaccine antigen: an H5 bird-flu protein containing a foreign fibritin foldon component.

• Chimera No. 2 — the challenge virus: an H5N1/PR8 influenza virus assembled from genome segments belonging to two different influenza viruses.

Between them sits BECC470s, the synthetic version of an engineered lipid-A molecule originally produced through the plague bacterium.

HHS Funds the Research

American taxpayers funded the research through several federal programs.

The study identifies support from an NIH/NIAID Adjuvant Development Contract:

HHS-NIH-NIAID-BAA2017 / HHSN272201800043C

The research also received support through NIAID CRIPT, the Centers of Excellence for Influenza Research and Response (CEIRR), NIAID CIVICS and NIH funding supporting operation of the University of Pittsburgh Regional Biocontainment Laboratory.

Lead author Devon Riley was separately supported by the U.S. Army’s Long-Term Health and Education Training Program.

The institutional network included the University of Maryland, University of Pittsburgh and Icahn School of Medicine at Mount Sinai.

The researchers explicitly place their work in the context of the threat posed by potentially pandemic avian influenza.

Their experimental vaccine is therefore part of a federally financed effort to develop medical countermeasures that could be used for future H5N1 pandemic response.

The study itself does not establish that HHS has selected this particular experimental formulation for public deployment or that Americans will be required to receive it.

It establishes something narrower but concrete: HHS is spending taxpayer money to develop and evaluate this technology as a prospective H5N1 vaccine.

Commercial IP

The taxpayer-funded research also connects to privately licensed intellectual property.

Senior author Robert K. Ernst disclosed that he is a founder and scientific adviser/consultant to TollereBio Corporation.

The mSphere paper states that TollereBio licensed University of Maryland-Baltimore intellectual property related to the data presented in the study.

The paper does not identify TollereBio as the manufacturer of a finished H5N1 vaccine.

It does establish that intellectual property associated with the federally supported research has already been licensed to a private company connected to one of the study’s senior researchers.

The chain therefore extends beyond the laboratory:

taxpayer-funded pandemic-vaccine research → university intellectual property → private commercial license.

Bottom Line

American taxpayers are financing H5N1 pandemic-preparedness research involving two chimeras, an engineered plague-bacterium derivative, inflammatory TLR4 activation, and privately licensed commercial IP.

The vaccine pairs a chimeric bird-flu antigen with BECC470s—a synthetic version of lipid A first engineered through Yersinia pestis—and researchers say they tested it against a separate H5N1/PR8 influenza chimera.

BECC470s is intentionally designed to retain inflammatory immune stimulation while reducing lipid A’s toxicity, yet the published acute-toxicity claim rests on a single-dose rabbit test for which virtually no methodological details are provided.

All of it is being developed with HHS/NIH/NIAID funding for future pandemic response, while related university intellectual property has already been licensed to a private company connected to the study’s senior author.

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The Trump administration has quietly filled two positions at the top of the federal government’s vertically integrated pandemic-response architecture.

Sara Brenner is now leading the Office of Pandemic Preparedness and Response Policy (OPPR).

Brenner arrives at OPPR with deep ties to the government’s COVID-19 orchestration, including what FDA describes as coordinating “whole-of-government operations” involving medical countermeasures, diagnostics, regulation, preparedness and data.

Rachel Idowu has been appointed National Security Council (NSC) director for bioresponse.

Idowu returns to the NSC after previously directing biological-threat surveillance and response during the H5N1 bird-flu era and providing input to a federally supported H5N1 modeling project.

The White House confirmed both appointments just last month.

Together, the two White House posts provide an unprecedented top-level mechanism for orchestrating pandemic “preparedness” and response across federal agencies whose surveillance, research, regulatory, procurement, and medical-countermeasure powers appear otherwise divided.

Brenner and Idowu arrive as this website for years has been exclusively documenting and warning of ongoing government-led influenza/bird flu pandemic orchestration in the post-COVID-19 era: simultaneous 1) federal threat-centric messaging about, surveillance of, and gain-of-function experiments on influenza/bird flu pathogens, and 2) influenza/bird flu countermeasure (vaccines and other drugs) development.

The same orchestration methodology characterized the COVID-19 pandemic and the government’s coronavirus projects in the preceding decades.

It’s worth mentioning:

• Trump appointed a military-grade influenza pandemic architect, Erica Schwartz, in April to lead the CDC.

• And that the Trump/Kennedy FDA this month approved Moderna’s mRNA-1010 influenza shot ‘mFLUSIVA’ despite showing less than 1% absolute benefit and six times more severe reactions.

• And that a new Trump admin policy permits funding gain-of-function experiments under a new definition, ‘Potential Dangerous GOF Research.’

• And that HHS is so confident an influenza pandemic will be declared that the agency is developing “Day One” influenza pandemic vaccines.

The appointments raise obvious questions:

• Why now?

• What is the government preparing this newly staffed orchestration layer to coordinate?

• And why have officials with deep COVID-19 and H5N1 ties been selected to lead it?

The significance of the appointments becomes even clearer when the positions are placed inside the government’s pandemic-response hierarchy.

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Congress Put OPPR at the White House

Congress created OPPR in 2022 and placed it inside the Executive Office of the President.

The law makes its director the president’s “principal advisor” on pandemic preparedness and response.

It also gives the director responsibility to coordinate Federal activities and provide strategic direction to applicable federal departments, agencies and White House offices.

In simplified linear form:

President
↓
OPPR / NSC
↓
HHS / DoD / USDA / DHS / other departments
↓
ASPR / BARDA / NIH / CDC / FDA / DoD components
↓
programs / contracts / laboratories / manufacturers

The departments retain their own legal and acquisition authorities.

But the White House sits above the departmental silos for government-wide coordination.

Trump has now filled both the OPPR and NSC bioresponse positions in that top layer.

Brenner Helped Coordinate the COVID Response

Brenner already has experience inside that kind of government-wide pandemic machinery.

Before COVID, she worked at the White House Office of Science and Technology Policy, coordinating biological and health-science policy across federal agencies and White House offices.

She subsequently moved to FDA.

Then COVID arrived.

FDA’s official biography says Brenner supported the national COVID-19 response from February 2020 through May 2023.

More importantly, FDA describes her experience as:

“coordinating whole-of-government operations”

involving medical countermeasures, technological innovation, regulation, public-health preparedness and data analytics.

Brenner also became the Diagnostic Data Lead for the HHS Data Strategy and Execution Workgroup, helping connect COVID diagnostic data, federal reporting systems, and government decision-making.

She eventually rose to acting FDA commissioner and principal deputy commissioner.

Trump has now put her in charge of the White House office legally responsible for coordinating the government’s next pandemic response.

Idowu Was at the NSC During H5N1 Narrative Introduction

Idowu comes from the surveillance and outbreak-response side of the system.

She is a CDC-trained epidemiologist who worked on Ebola response, helped develop Africa CDC, and worked extensively on infectious-disease surveillance.

Then, from 2024 through 2025, Idowu served as the NSC’s:

Director of Biological Threat Surveillance & Response

That put Idowu inside the White House purported biological-threat apparatus during the federal response to H5N1 bird flu.

But her H5N1 connection goes further than overlapping dates.

Idowu Advised an H5N1 Modeling Project

During Idowu’s NSC tenure, University of Virginia researchers developed DiTTO (Digital Twin for Transboundary OneHealth).

The federally supported system digitally modeled interactions among:

wild birds → livestock → farms → humans

and incorporated H5N1 incidence data to study avian-influenza transmission and risk.

The project’s acknowledgments specifically thank Rachel Idowu, another NSC official, OPPR personnel and CDC officials for:

“thoughtful comments and suggestions.”

The project received support associated with CDC, the Defense Threat Reduction Agency, USDA, and NSF.

We cannot say Idowu commissioned or controlled DiTTO.

We can say she personally provided input to an H5N1-relevant federal modeling project while serving as the NSC official responsible for biological-threat surveillance and response.

Trump has now returned her to the NSC as director for bioresponse.

Two Pandemic Nodes, Filled at Once

The appointments therefore create a remarkably simple chain.

Sara Brenner

COVID response
→ diagnostics/data
→ medical countermeasures
→ “whole-of-government operations”
→ OPPR

Rachel Idowu

outbreak surveillance
→ NSC biological-threat surveillance
→ H5N1 response period
→ H5N1 modeling input
→ NSC Bioresponse

And both now sit here:

PRESIDENT TRUMP
↓
WHITE HOUSE PANDEMIC/BIORESPONSE COORDINATION
↓
BRENNER — OPPR | IDOWU — NSC
↓
HHS | DoD | USDA | DHS | other agencies
↓
surveillance | research | regulation | countermeasures | procurement | response

Bottom Line

Trump has now filled two positions at the top of the federal government’s vertically integrated pandemic-response apparatus.

Brenner previously helped coordinate whole-of-government COVID-19 operations and now leads the White House office Congress created to coordinate pandemic activity across federal agencies.

Idowu previously directed biological-threat surveillance and response at the NSC during the H5N1 bird-flu era, provided input to a federally supported H5N1 modeling project, and has now returned as NSC director for bioresponse.

Their appointments also follow Trump’s selection of Erica Schwartz, whose military career included influenza pandemic preparedness, to lead the CDC.

The personnel now taking control of key nodes in the government’s pandemic apparatus therefore carry backgrounds spanning COVID-19 orchestration, H5N1 surveillance and modeling, and influenza pandemic preparedness.

That convergence comes as the same federal machinery beneath them continues to simultaneously surveil and “study” influenza threats while developing vaccines, drugs, and other countermeasures for them.

The question is no longer whether the government possesses a vertically integrated apparatus capable of orchestrating another pandemic response.

It does.

The question is what that apparatus is now being positioned to do.

And why Trump has chosen officials with such direct COVID-19 and influenza/bird-flu backgrounds to lead its most consequential nodes.

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The government-funded EcoHealth-Wuhan Institute of Virology (WIV)-Ralph Baric research network at the center of the COVID-19 origins controversy spent years before the pandemic helping build the SARS-related coronavirus reference space that would later be used to explain SARS-CoV-2’s supposed evolutionary history.

At the same time, the network was constructing experimental SARS-related coronaviruses and ultimately proposed creating SARS-CoV-2-like experimental systems in the 2018 DARPA proposal known as DEFUSE.

Then SARS-CoV-2 appeared.

The EcoHealth-WIV side of the collaborating network was heavily involved in building the digital coronavirus sequence reference space, while the Baric-UNC side supplied the reverse-genetics capability for turning selected digital sequences from that space into experimental outputs.

And the coronavirus landscape this network itself had spent years building became part of the “evidence” used to give the pandemic virus its evolutionary backstory.

This does not mean the network generated every coronavirus sequence in the global reference space.

It means the network deliberately helped build the SARS-related portion relevant to SARS-CoV-2 by discovering, sequencing, characterizing, and experimentally working with important members of that reference landscape.

Put simply: the government-funded network suspected of potentially having a role in SARS-CoV-2’s origin had spent years building both SARS-CoV-2-like experimental viruses and also the supposed natural-virus reference space later used to “explain” SARS-CoV-2.

• They deliberately built the sequence map before the pandemic.

• They also deliberately experimented with, and ultimately proposed creating, SARS-CoV-2 viruses inside the territory represented by that map.

• Then SARS-CoV-2 appeared, and their map was used to explain where it came from.

That creates a circularity problem.

The network’s own pre-pandemic reference space becomes evidence for SARS-CoV-2’s supposed natural history, while SARS-CoV-2’s apparent fit within that same reference space is then treated as validation of the history the network’s reference space supplied.

Put another way, the result is a self-reinforcing evidentiary loop.

Their reference space helps establish SARS-CoV-2’s supposed natural backstory, and SARS-CoV-2’s ability to fit within their reference space is then used as evidence that the backstory is true.

The circularity raises substantive methodological, transparency, and provenance questions.

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They Built the SARS-Related Coronavirus Reference Space

Before COVID-19, WIV and EcoHealth researchers were actively discovering and sequencing SARS-related coronaviruses.

A 2017 WIV-EcoHealth study, for example, reported a “rich gene pool” of bat SARS-related coronaviruses and deposited 11 newly identified complete genomes into GenBank.

The work was funded in part by the NIAID (R01AI110964) and USAID Emerging Pandemic Threats (AID-OAA-A-14-00102) PREDICT program.

The study reads:

“Based on the diversity of RBD sequences, 11 novel SARSr-CoV strains named by abbreviation of bat species and sample ID (Rs4081, Rs4084, Rs4231, Rs4237, Rs4247, Rs4255, Rs4874, Rs7327, Rs9401, Rf4092 and As6526) were selected for full-length genomic sequencing based on sample abundance, genotype of RBD as well as sampling time.”

These digital constructs helped define the known sequence universe against which future SARS-related coronaviruses could be compared.

That matters because scientists cannot directly observe SARS-CoV-2’s ancestry.

They infer it by comparing its sequence to earlier coronavirus sequences and constructing evolutionary relationships among them.

The reference space helps create the backstory.

The Same Network Was Building Experimental Viruses Inside That Space

The network was not merely cataloging natural coronavirus sequences.

In 2015, Baric, WIV’s Zhengli Shi and collaborators reported using the SHC014 spike in a SARS-CoV reverse-genetics system to create a chimeric virus.

The paper states that researchers “generated and characterized a chimeric virus expressing the spike of bat coronavirus SHC014 in a mouse-adapted SARS-CoV backbone.”

The purported resulting virus was said to be able to use human ACE2 and replicate in primary human airway cells.

So by years before COVID-19, the same ecosystem was doing both:

• discover SARS-related viruses → sequence them → add them to the known coronavirus landscape

and

• take SARS-related sequences → construct new experimental viruses.

The distinction between the reference space and the experimental space was therefore already blurred.

Then They Proposed SARS-CoV-2-Like Viruses in DEFUSE

By 2018, the overlap became even more striking.

That year, DARPA launched PREEMPT, a program designed to build the evolutionary and wildlife alibis of future pandemic pathogens before the pandemics occurred—predicting their alleged evolutionary trajectories, species-jump mutations, animal reservoirs, spillover pathways, and geographic origins in advance.

Then EcoHealth, WIV, Baric and collaborators submitted DEFUSE directly into PREEMPT.

The pre-COVID-19 pandemic proposal described coronavirus experiments specifically involving the same characteristics SARS-CoV-2 would have: receptor-binding domains, the introduction of proteolytic cleavage sites into spike proteins and 2P-stabilized spike vaccine constructs, along with synthetic reconstruction of SARS-related coronavirus genomes.

Then, less than two years later, SARS-CoV-2 appeared.

Its receptor-binding domain and furin cleavage site became central molecular features in the origins debate.

And the 2P spike construct became central to the SARS-CoV-2 vaccine response.

The point is not that DEFUSE contained the SARS-CoV-2 genome.

The point is that the same network building the supposed natural evolutionary landscape of SARS-related coronaviruses was simultaneously proposing to create experimental SARS-CoV-2-like viruses with a combination of features that would shortly become conspicuous in SARS-CoV-2 and its countermeasures.

Then Their Reference Space Became SARS-CoV-2’s Backstory

The sequence of events is remarkably simple:

• Before COVID-19: The controversial network claims it spent years discovering and sequencing SARS-related coronaviruses, helping construct the reference space of allegedly known coronavirus sequences.

• At the same time: The network used those kinds of sequences to construct experimental SARS-CoV-2-like viruses said to be capable of infecting human cells.

• By 2018: The network proposed additional SARS-CoV-2-like coronavirus systems in DEFUSE specifically involving RBDs, cleavage sites, and 2P spike constructs.

• Then: SARS-CoV-2 appeared, containing RBDs, cleavage sites, and 2P spike constructs.

• Afterward: Scientists used the preexisting SARS-related coronavirus reference space to establish SARS-CoV-2’s supposed evolutionary alibi and natural backstory.

Bottom Line

The government-funded network at the center of the COVID-19 origins controversy spent years before the pandemic doing two things simultaneously:

building the SARS-related coronavirus reference space and building experimental SARS-related coronaviruses inside that same sequence space.

By 2018, members of that network were proposing SARS-CoV-2-like experimental systems in DEFUSE involving RBDs, inserted cleavage sites, and 2P spike constructs.

Then SARS-CoV-2 appeared, with those very features.

And the SARS-related coronavirus landscape this network had deliberately spent years constructing became part of the reference framework used to give SARS-CoV-2 its supposed natural evolutionary backstory.

That creates a self-reinforcing evidentiary loop: the network’s reference space is used to establish SARS-CoV-2’s supposed natural history, while SARS-CoV-2’s ability to fit within that reference space is then treated as evidence that the history is correct.

The question is therefore not whether this network helped construct SARS-CoV-2’s evolutionary reference framework before the pandemic.

It did.

What remains unresolved is whether that reference-building program was genuinely independent of the experimental coronavirus program developing alongside it.

Or whether the two were connected in ways that have not been fully disclosed.

The people building SARS-CoV-2-like experimental viruses cannot have their own coronavirus reference space treated as independent evidence for SARS-CoV-2’s provenance without first establishing the provenance of that reference space itself.

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More than six years before COVID-19, DARPA awarded Pfizer approximately $7.7 million to develop a platform for rapidly protecting people against emerging infectious threats, according to the U.S. Department of Defense website.

The 2013 contract was HR0011-14-3-0001.

Two years after the award was announced, DARPA explicitly identified that exact Pfizer contract as part of ADEPT—Autonomous Diagnostics to Enable Prevention and Therapeutics.

ADEPT was part of DARPA’s broader pre-COVID pandemic technology stack, which sought to use digital sequences representing the purported genetic information of future pathogens—without direct observation of those future pathogens—to predict their evolution and develop countermeasures, including vaccines, “in advance of need.”

Pfizer wasn’t the only future COVID-19 mRNA vaccine manufacturer there.

Moderna was an ADEPT performer, too.

Both became central to the international COVID-19 response.

Yet while Moderna’s pre-COVID relationship with DARPA has become widely known, Pfizer’s documented place inside the same DARPA ADEPT program has received remarkably little attention.

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2013: DARPA Awards Pfizer $7.7 Million

The first primary source is a December 4, 2013 Department of Defense contract announcement.

It says DARPA awarded Pfizer a three-year agreement worth $7,672,468 under HR0011-14-3-0001.

The Defense Department said Pfizer would develop a platform to:

“identify and subsequently induce the production of protective antibodies to an emerging pathogen directly in an infected or exposed individual.”

The stated objective bears a striking resemblance to the claimed function of Pfizer’s later COVID-19 vaccine: inducing the recipient to produce antibodies against an emerging pathogen.

Whether HR0011-14-3-0001 involved the same technology or an adjacent platform remains unclear from the publicly available contract description.

2015: DARPA Identifies Pfizer’s Contract as ADEPT

The second primary source is DARPA’s FY2015 funding record.

It contains the exact same contract number, HR0011-14-3-0001, and identifies:

Pfizer Inc.

Program: ADEPT

Office: Biological Technologies Office

Program Manager: Daniel Wattendorf

The document records $664,225 obligated during FY2015 under Pfizer’s contract.

That is not the $7.7 million total agreement announced in 2013.

What makes the 2015 document important is much simpler:

DARPA itself identifies Pfizer contract HR0011-14-3-0001 as ADEPT.

Moderna & UNC Were in ADEPT, Too

The same 2015 DARPA funding record also identifies Moderna Therapeutics under ADEPT, BTO and Daniel Wattendorf.

It also identifies the University of North Carolina at Chapel Hill under the same program, office, and program manager.

That UNC award belonged to J. Michael Ramsey’s ReMeDx diagnostic project, according to a 2016 UNC Lineberger/UCRF legislative report.

Ramsey’s own 2018 UNC CV lists the broader DARPA award at $19,569,564.

The same CV shows that Pfizer had separately funded Ramsey’s laboratory, including a project titled:

“Microfluidics for Biologics Characterization.”

So DARPA’s 2015 records place Pfizer, Moderna and Ramsey/UNC inside ADEPT, while Ramsey also had a documented independent funding relationship with Pfizer.

The Larger DARPA Pandemic Stack

That network becomes more significant alongside DARPA’s other pre-COVID pandemic programs.

DARPA’s PROPHECY program pursued the claimed ability to “predict” future viral evolution without direct observation and ultimately develop countermeasures “in advance of need.”

UNC’s Ralph Baric coronavirus ecosystem intersected with that work through research combining coronavirus bioinformatics, synthetic genomics, reverse genetics, spike/RBD and host-range experimentation.

Then came DEFUSE in 2018.

DEFUSE is particularly significant because the pre-COVID DARPA proposal involving Baric put multiple characteristics later matching SARS-CoV-2 and its spike-based countermeasure architecture into the same document: SARS-related coronavirus RBD manipulation, proteolytic cleavage-site insertion, prefusion-stabilized spike vaccine work and synthetic coronavirus genomes assembled as six contiguous cDNA pieces.

The six-piece proposal was also technically notable because Baric’s laboratory had pioneered six-piece coronavirus reverse genetics years earlier.

Then, in December 2019, just before the outbreak was reported, NIAID and Moderna entered a Material Transfer Agreement with Baric’s UNC laboratory to provide him with jointly owned mRNA coronavirus vaccine candidates for experimental challenge studies.

The point isn’t that these were all one DARPA contract.

They weren’t.

The significance is the pre-COVID network: DARPA was already funding Pfizer and Moderna under ADEPT, funding Ramsey’s UNC diagnostic work, and operating a broader pandemic technology stack that repeatedly intersected with Baric’s coronavirus research.

Bottom Line

The pre-COVID DARPA pharmaceutical story has largely focused on Moderna.

Government records show that picture is incomplete.

In 2013, DARPA awarded Pfizer approximately $7.7 million under HR0011-14-3-0001 for what the Defense Department described as a platform to “identify and subsequently induce the production of protective antibodies” against an emerging pathogen.

In 2015, DARPA explicitly identified that exact Pfizer contract as ADEPT.

The same DARPA funding record identifies Moderna and Ramsey’s UNC project under ADEPT, while separate primary records document Pfizer’s funding relationship with Ramsey.

Those connections sat alongside a broader pre-COVID DARPA pandemic technology stack that also intersected with Baric’s coronavirus prediction, synthetic-genomics and reverse-genetics work—and later DEFUSE, which proposed multiple SARS-CoV-2-like features before the pandemic began.

The well-known DARPA-Moderna relationship was therefore only one part of a much larger pre-COVID network.

Pfizer was already inside ADEPT, too.

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In July 2025, the Defense Advanced Research Projects Agency (DARPA) announced two programs that, read together, form a pipeline for turning indirect machine readouts alone of purported “unknown biothreat” samples into operationalizable digital protein sequences—using algorithms rather than direct observation to determine the sequences that will digitally represent those alleged threats—then “predicting” what the digitally represented biothreats supposedly do and characterizing them as threats now requiring government response, with the ultimate goal of using those digital reference sequences to accelerate medical countermeasures, potentially including vaccines.

The revelation comes as mainstream news outlets promote messaging claiming that artificial intelligence has been used “to design brand new viruses that are fully functional and can replicate in the laboratory,” indicating a broader mainstream narrative increasingly conditioning the public to regard purported AI-generated biological agents as a new and credible class of threat.

The first program, PROtein SEquencing (PROSE), was announced by DARPA’s Microsystems Technology Office on July 9, 2025.

Twenty-two days later, DARPA’s Biological Technologies Office announced the Network of Optimal Dynamic Energy Signatures (NODES) program.

PROSE and NODES are separate programs administered by separate DARPA offices.

But their respective outputs and inputs line up.

PROSE is developing technology to turn machine readings of purportedly threatening unknown protein samples into a digital amino-acid sequence.

NODES is developing computer models that begin with protein sequences and “predict their associated functions.”

DARPA’s solicitations reviewed for this report do not explicitly state that PROSE outputs will be fed into NODES.

But they describe two programs announced 22 days apart whose capabilities could operate sequentially:

purported unknown biothreat sample → machine signals → algorithmically called digital sequence → computationally predicted function → threat characterization → medical countermeasures.

It all ends with medical interventions and government “threat” response.

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DARPA Says It Needs New Technology to ‘Read’ Previously ‘Unknown’ Biothreats

PROSE begins with an extraordinary premise.

DARPA says the Department of Defense cannot rapidly identify certain purported biological threats, including threats that are “unknown.”

According to the agency’s BAA (found here):

“The Department of Defense (DoD) lacks the ability to rapidly detect and identify unknown biothreats, such as natural and engineered protein-based biotoxins.”

DARPA then claims artificial intelligence and synthetic biology are creating a new class of purported biological threats:

“New capabilities in synthetic biology and artificial intelligence (AI) enable the rapid design and production of novel protein-based biothreats.”

The agency says freely available AI protein-design tools can:

“generate thousands of re-engineered toxin molecules with similar structure, and likely function, as the parental toxin, but with such radically different sequences that they are unidentifiable as threats with current technology.”

DARPA says PROSE will:

“develop a high throughput sequencing technology to fill this critical gap in the DoD’s threat response capabilities.”

But the purported threats DARPA wants PROSE to “read” do not necessarily have known genomes, or even genetic material.

According to the BAA:

“While current methods can rapidly identify threats with known genomes, we lack the capability to read biothreats that do not have genetic material, such as biological toxins, or threats with currently unreadable modifications that augment function.”

DARPA nevertheless says PROSE will:

“enable the identification and characterization of these threats”

and provide technology for:

“threat/pathogen identification, warfighter health, in-field decision-making, and intelligence gathering.”

The program description then identifies what PROSE is supposed to “read”:

“PROSE will demonstrate molecular readers that can accurately read a broad range of amino acids and post-translational modifications (i.e., letters) in sequence for unknown protein samples.”

DARPA says those readers must handle sequences of at least 300 “letters,” at claimed accuracy of at least 99%, and at a throughput of at least 10¹⁰ letters per day.

And DARPA anticipates that synthetic biology will dramatically expand what can count as a “letter”:

“emerging techniques in synthetic biology are expected to increase the number of possible letters exponentially over the next decade.”

PROSE readers must therefore demonstrate:

“scalability across a broad range of chemical complexity.”

DARPA says accomplishing this will require new protein read elements, microsystems, translocation approaches, measurement technologies, and algorithms.

But what exactly will these new molecular readers directly observe?

PROSE Does Not Directly Observe the Sequence It Reports

This is where the distinction between measurement and interpretation becomes critical.

PROSE does not directly observe a string of amino-acid letters inside the purported biothreat sample.

The proposed machines generate signals.

Software then determines what those signals supposedly mean.

DARPA explicitly requires:

“algorithms for letter calling capable of translating raw signal data into a specific amino acid sequence.”

DARPA calls this process “letter calling.”

The agency describes PROSE as demonstrating molecular readers for purported “unknown protein samples,” followed by:

“advanced algorithms that translate signals from the integrated system into letter calls.”

The distinction is fundamental.

The machine signal is the measurement.

The amino-acid letter is the algorithm’s interpretation of that measurement.

And the sequence is assembled from those interpreted letters.

Electrical Current & Fluorescence Become Biological ‘Letters’

DARPA’s solicitation provides examples of what these indirect measurements can look like.

Discussing existing nanopore and optical technologies, DARPA explains that chemical information can be transduced into:

“a change in current in a circuit, or a fluorescence signal read by a camera.”

Those signals are not amino acids.

They are electrical or optical outputs interpreted as corresponding to amino acids.

PROSE is intended to push this principle considerably further by developing “novel measurement modalities” capable of distinguishing a much larger universe of purported protein “letters.”

DARPA explicitly describes the final computational step as:

“advanced algorithms to translate integrated system signals into a called sequence of letters.”

The proposed chain is therefore:

purported “biothreat” protein sample

↓

physical interaction with a reader

↓

machine signal

↓

algorithm

↓

amino-acid “letter call”

↓

quality/accuracy score

↓

digital protein sequence

DARPA even requires performers to document the computational models responsible for:

“translating raw signals into amino acid calls”

including their architecture, training data, and performance metrics.

In other words, the digital sequence is not the raw machine observation.

It is the product of an interpretive computational process applied to the raw machine output.

The Digital Sequence Can Then Stand for the Alleged Biothreat

That distinction becomes considerably more consequential because PROSE is specifically intended to operate where an existing reference sequence may not exist.

DARPA starts with something it characterizes as an unknown biothreat.

The sample generates machine signals.

Algorithms translate those signals into “letter calls.”

Those calls become a specific digital amino-acid sequence.

For downstream systems and decision-makers that do not independently possess and examine the originating physical sample, the digital sequence can therefore become the operationalizable representation of the purported threat.

And DARPA explicitly intends PROSE for consequential applications.

The agency lists, as mentioned above:

“threat/pathogen identification, warfighter health, in-field decision-making, and intelligence gathering.”

The digital representation of the sequence is therefore not merely an academic description stored on a researcher’s computer.

DARPA is developing PROSE so its outputs can be operationalized.

And 22 days after announcing PROSE, DARPA announced another program built to start with protein sequences.

Twenty-Two Days Later, DARPA Announced NODES

NODES begins farther downstream.

It does not need to begin with the original purported biothreat sample.

Its stated input is a protein sequence.

DARPA says in its NODES BAA (found here) that the project will:

“develop computational models that will input protein sequences and predict their associated functions.”

That means the output datatype PROSE is being developed to produce is precisely the datatype NODES is being developed to consume.

PROSE: machine signals → digital protein sequence

NODES: digital protein sequence → predicted function

Again, DARPA’s solicitations do not establish that the agencies will specifically feed PROSE-generated sequences directly into NODES.

But the technical compatibility is unmistakable.

The two programs were announced just 22 days apart.

NODES Starts With Protein Sequences & ‘Predicts Their Associated Functions’

Where PROSE is designed to produce a digital protein sequence from machine signals, NODES begins with the protein sequence.

DARPA states:

“The Defense Advanced Research Projects Agency (DARPA) is soliciting proposals to develop computational models that will input protein sequences and predict their associated functions based on protein movements (dynamics) observed during folding, protein binding, and/or allosteric interactions.”

DARPA says those predictions will then be tested under conditions determined by the agency:

“The ability to predict protein function will also be tested across a range of scenarios defined by DARPA.”

The models will not merely simulate molecular dynamics.

According to DARPA, performers will be engaged in:

“simulating, learning, and generating molecular dynamics”

and will be required to create:

“an Application Programming Interface (API) for general usage by the community”

along with:

“appropriate guardrails to ensure the safety of both the models and the interpretation of the predicted protein functions.”

DARPA then explicitly connects those computational predictions to de novo sequences, unknown agents, threat characterization, and medical countermeasures:

“Together, these efforts will bolster the Department of Defense’s (DoD) ability to probe the limitless space of de novo protein sequences, provide a tool to expedite threat characterization when the nation or warfighters are introduced to an unknown agent, and shorten the time to developing Medical Countermeasures (MCMs).”

DARPA also says NODES will:

“support biomedical research by providing expedited ways to understand infectious, protect crops, develop new pharmaceuticals, and elucidate mechanism of disease.”

The sequence DARPA describes is therefore explicit:

protein sequence → computational prediction → interpretation of predicted function → “unknown agent” → “threat characterization” → Medical Countermeasures.

DARPA Calls Protein-Function Determination a ‘Universal Design Tool for Controlling Biology’

DARPA’s description of why it wants this capability is even more striking.

The agency begins:

“Proteins are essential building blocks of life, playing a crucial role in countless biological processes.”

DARPA says their universality makes them useful for medicine:

“The universality of proteins across lifeforms makes them a powerful tool in medicine, where their tunable properties can be harnessed to develop life-saving treatments.”

But DARPA immediately presents the other side:

“Conversely, proteins are also implicated in toxic functions and their misuse can prove detrimental to life.”

It then states what determining protein functions could ultimately provide:

“The determination of protein functions, therefore, offers the opportunity to develop a universal design tool for controlling biology in areas of early preparedness, rapid response, and derisking applications.”

And DARPA explicitly connects that capability to surveillance and national security:

“Pushing the envelope in these areas is crucial to strengthen our bio surveillance, security, and attribution efforts.”

So DARPA is not merely proposing software that assigns academic descriptions to protein sequences.

The agency itself describes protein-function determination as an opportunity to develop a “universal design tool for controlling biology” for “early preparedness” and “rapid response,” while strengthening “bio surveillance, security, and attribution efforts.”

Deep Learning Will ‘Generalize Predictions’ & ‘Rapidly Infer’ From a Given Sequence

And NODES’ computational predictions do not end with its initial models.

After creating what DARPA calls a:

“library of molecular movements”

the agency says:

“deep learning methods are sought to generalize predictions and rapidly infer an ensemble of structures with correct thermodynamics, energy, and force field-based weights for a given sequence.”

That final phrase returns NODES to its fundamental input:

“a given sequence.”

From that sequence, DARPA proposes computational models that “predict” function, systems that “simulate,” “learn,” and “generate” molecular dynamics, and deep-learning methods that “generalize predictions” and “rapidly infer” structures.

Those computational outputs are then intended to help DARPA “probe the limitless space of de novo protein sequences,” characterize an “unknown agent,” strengthen “bio surveillance, security, and attribution,” and shorten the time to Medical Countermeasures.

Read directly after PROSE, the potential handoff is stark:

• PROSE: machine signals → algorithmic “letter calls” → digital protein sequence.

• NODES: digital protein sequence → “generalize predictions” → “rapidly infer” → “predict their associated functions” → “threat characterization” → Medical Countermeasures.

PROSE & NODES Form Two Halves of the Same Potential Pipeline

DARPA announced PROSE on July 9, 2025.

It announced NODES 22 days later.

PROSE seeks to solve one problem: How can machine signals from an unknown sample be converted into a protein sequence when no reference sequence exists?

NODES begins with the resulting datatype and asks another: Given a protein sequence, how can computation predict what it does?

The first produces the digital representation.

The second operates on digital representations.

Both are explicitly directed toward purported unknown or novel biological threats.

And both are connected to consequential government response capabilities.

DARPA has not stated in the solicitations reviewed here that PROSE and NODES constitute one formally integrated program.

But read together, their respective inputs and outputs reveal a technically compatible pipeline:

PURPORTED ‘UNKNOWN’ BIOTHREAT

↓

PROSE

INDIRECT MACHINE READOUT

↓

algorithmic “letter calling”

↓

OPERATIONALIZABLE DIGITAL SEQUENCE

↓

NODES

digital sequence as input

↓

computational modeling / AI

↓

“PREDICT THEIR ASSOCIATED FUNCTIONS”

↓

“THREAT CHARACTERIZATION”

↓

GOVERNMENT RESPONSE

↓

MEDICAL COUNTERMEASURES

Bottom Line

DARPA is developing systems in which purported “unknown biothreats” can be assigned digital sequences and alleged predicted functions that feed into “threat characterization,” government response, and medical countermeasures—without direct observation of either the sequence or function being claimed.

PROSE turns indirect machine signals into algorithmically called sequences. NODES takes protein sequences and uses deep learning to “generalize predictions,” “rapidly infer” structures, and “predict their associated functions.”

The concern is what happens downstream.

• How much confidence and government authority should be invested in an alleged threat whose digital identity is derived from machine signals and whose alleged function is computationally predicted?

• What evidentiary threshold turns those inferences into “threat characterization”?

• And when does that characterization become sufficient to drive government response or medical countermeasures?

The ultimate question is simple:

When does an algorithmic inference about an “unknown biothreat” become an operationalizable government fact—and what happens when the government acts on it?

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Yesterday, Moderna, Inc. announced that the U.S. Food and Drug Administration (FDA) has “approved mFLUSIVA® (mRNA-1010), a new vaccine against seasonal influenza, for use in all adults 50 years and older.”

Even though the alleged benefit for adults 65 and older was never proven.

The move comes after the FDA’s Vaccines and Related Biological Products Advisory Committee (VRBPAC) voted unanimously in June that the “benefits” of Moderna’s experimental mRNA influenza vaccine “outweigh its risks.”

It also comes after President Trump selected a military-grade influenza pandemic architect, Erica Schwartz, to run the CDC.

Moderna expects to have the drug available for eligible populations in the U.S. for the 2026-2027 respiratory virus season.

Moderna’s own scientists confirmed in a May New England Journal of Medicine publication that mFLUSIVA triggers six times more severe reactions for less than 1% alleged absolute benefit.

The approval raises obvious safety and informed-consent questions about how long the body produces the purportedly encoded influenza proteins, what their long-term biological effects may be, and whether the product or its outputs could be shed from the original recipient onto others through exosomes or extracellular vesicles in excreted bodily fluids.

A 2021 Journal of Extracellular Vesicles study found that cells expressing a target protein can release extracellular vesicles that “carry” that protein, demonstrating that what is produced inside the body can be exported outside the cell in membrane-bound particles.

A separate 2023 Pharmaceutics review explains that exosomes are natural transport vesicles that carry proteins and nucleic acids throughout the body and are present in bodily fluids including blood, saliva, and other secretions—meaning they are not confined to the original cell or even the original location in the body.

You can contact the FDA here.

U.S. taxpayers funded the research behind mRNA-1010.

The FDA falls under Health and Human Services (HHS), which is run by Secretary Robert F. Kennedy Jr.

HHS is currently pursuing “day one” influenza pandemic vaccines, while the agency also funds experiments determining how to make influenza more pathogenic, investing American tax dollars in both the problem and the solution.

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FDA Approved mFLUSIVA for Seniors Before Clinical Benefit Was Confirmed

While FDA granted traditional approval for adults 50 through 64, adults 65 and older received accelerated approval based on immune responses.

Moderna states:

“Use in adults 65 years of age and older was granted accelerated approval.”

The company says the supporting trial enrolled 2,992 adults and compared mFLUSIVA’s immunogenicity against a high-dose inactivated influenza vaccine.

But clinical benefit has not yet been confirmed.

According to Moderna:

“Confirmation of clinical benefit in this population will be assessed in a postmarketing clinical trial.”

Moderna repeats:

“Continued approval for this indication may be contingent upon verification and description of clinical benefit in a confirmatory trial.”

In other words, FDA approved mFLUSIVA for Americans 65 and older before the clinical benefit required to maintain that approval has been confirmed.

FDA Used Different Evidence for Different Age Groups

The approval announcement reveals that FDA relied on two different evidentiary pathways.

For adults 50 through 64, approval was based on the 40,805-person Phase 3 trial measuring relative vaccine efficacy against a standard-dose influenza vaccine.

For adults 65 and older, accelerated approval was based on immune responses compared with a high-dose influenza vaccine.

Clinical benefit in the older population is to be confirmed after approval.

Bottom Line

FDA approved Moderna’s first seasonal mRNA influenza vaccine for adults 50 and older.

For adults 65 and older, the agency granted accelerated approval before clinical benefit was confirmed.

Moderna says that benefit will be tested after the product is already on the market.

The company’s own trial data also showed substantially more severe reactions for less than 1% alleged absolute benefit.

At the same time, the product instructs cells to produce influenza proteins inside the body, while the approval record cited here does not establish:

• how long that production continues;

• where the resulting proteins travel;

• whether they accumulate in organs or tissues;

• whether they are packaged into exosomes or other extracellular vesicles;

• how long those vesicles remain in circulation;

• whether they enter saliva, urine, breast milk, semen or other bodily fluids;

• or whether any biologically active product-derived material can pass from recipients to other people.

Published research already establishes that cells can export proteins through extracellular vesicles and that those vesicles circulate through bodily fluids.

That does not, by itself, prove person-to-person shedding from mFLUSIVA.

But it does establish the biological pathway FDA and Moderna would need to test before assuring the public that shedding cannot occur.

Instead, FDA approved the product for millions of older Americans—including a population for whom clinical benefit remains unconfirmed—without publicly resolving the duration, distribution, persistence, exosomal transport, or potential external release of what the injection causes the body to produce.

That is not merely a question of vaccine effectiveness.

It is a question of whether recipients, and people around them, have been given enough information to provide meaningful informed consent.

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Modern coronavirus engineering rests on an earlier premise: that two coronaviruses can naturally contribute portions of what researchers regard as their genetic material to produce a new coronavirus.

That premise entered the literature in a 1985 Journal of Virology paper co-authored by Michael M. C. Lai and Ralph S. Baric, not through direct observation of coronavirus genomes combining, but through interpretation of indirect laboratory readouts.

The paper and the work it described were funded by U.S. government grants RG 1449-A-1, PCM-4507, and Al 19244.

Baric is the U.S. taxpayer-funded coronavirologist whose decades of pre-2019 work claimed to explain down to the molecular level how to build coronaviruses with the defining properties (here) of the purported COVID-19 pandemic virus, SARS-CoV-2, before it was said to have emerged: having six segments separated by five restriction sites and a spike protein with optimized furin cleavage sites, receptor-binding domains, and two-proline vaccine constructs.

The complete provenance of the SARS-CoV-2 genome as presented to us by mainstream institutions has never been made public, meaning we cannot independently evaluate the complete evidentiary chain underlying that genome before it became the foundation for authoritarian government-led COVID-19 pandemic response decisions.

That raises a broader question about the evidentiary foundation of coronavirus genetics as a whole and its relation to the government.

Lai, Baric’s predecessor and the government-funded “father of coronavirus research,” had already established how to attribute molecular and genetic properties to coronaviruses through interpretation of laboratory measurements rather than direct observation.

In the 1985 paper, Lai, Baric, and their co-authors extended that framework by claiming that two coronavirus strains could contribute different portions of what they regarded as their genomes to produce a new coronavirus.

But the paper confirms that claim did not come from directly observing two coronavirus genomes combining.

It came from interpreting indirect laboratory readouts generated from mixtures of biological material that had been cultured, radiolabeled, centrifuged, chemically processed, enzymatically digested, and separated through electrophoresis.

That raises a fundamental question:

If modern coronavirus engineering rests on the premise that coronaviruses naturally combine portions of what researchers regard as their genetic material to produce new coronaviruses, and that premise itself entered the literature through interpretation of indirect laboratory readouts rather than direct observation, how much of modern coronavirus genetics ultimately rests on inherited interpretations rather than directly observed phenomena?

Moreover, COVID-19 lockdowns, mask mandates, vaccine campaigns, social-distancing policies, case counts, diagnostic testing, and countless other government decisions all rested, in part, on the molecular understanding of coronavirus presented by modern coronavirology.

Which raises another question:

If later science and policy depended on a particular scientific framework, and this article traces one of that framework’s foundational premises back to its origin, what should we conclude about the evidentiary basis of the framework itself?

And:

Why did the U.S. government spend decades funding the development of a coronavirus molecular framework whose foundational premises are based on interpretation rather than direct observation, then later rely on that same framework to justify lockdowns, mask mandates, vaccine campaigns, social distancing, diagnostic testing, and countless other COVID-19 response policies?

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Lai Had Already Attributed Genetic Properties to Coronavirus

The 1985 paper did not begin from scratch.

Seven years earlier, Lai and Stohlman’s taxpayer-funded 1978 paper, RNA of Mouse Hepatitis Virus, attempted to establish what coronavirus was understood to be at the molecular level.

The researchers collected material from treated cell cultures, separated portions of it through centrifugation, chemically processed the recovered material, and subjected it to laboratory tests.

Those procedures produced measurements:

• sedimentation behavior;

• migration patterns;

• RNase sensitivity;

• and binding in a poly(A) assay.

They did not directly observe what the paper ultimately described as the coronavirus genome.

The authors instead interpreted those laboratory measurements as indicating that coronavirus possessed one large, single-stranded RNA molecule that was “probably positive stranded” and had a particular estimated molecular weight and coding capacity.

Those attributed genetic properties became part of the molecular framework inherited by later coronavirology.

The field increasingly stopped asking whether coronavirus possessed the proposed genome described by Lai and began asking how that genome was organized, transcribed, recombined, reconstructed, and manipulated.

The 1985 Paper Asked Whether Coronaviruses Could Exchange Genetic Material

The 1985 paper explicitly framed the unresolved question:

“Whether such an exchange of genetic material can also occur in RNA viruses with nonsegmented genomes has been debatable.”

The authors acknowledged that earlier attempts to demonstrate the claimed phenomenon had been “largely unsuccessful.”

They wrote:

“Various attempts to demonstrate RNA-RNA recombination in either RNA phages or animal viruses have been largely unsuccessful.”

They further stated that, outside of one group of agents called picornaviruses, the claimed phenomenon had not previously been observed:

“This phenomenon, however, has so far not been observed with other RNA viruses.”

The paper therefore sought to establish that two strains of mouse hepatitis virus (A59 and JHM) could contribute different portions of what the authors regarded as their genomes to produce a third coronavirus containing parts attributed to both.

What the Researchers Physically Did

The researchers placed cultured DBT or L-2 cells in wells and added preparations identified as temperature-sensitive mutants of the A59 and JHM coronavirus strains.

They maintained the cells under different temperature conditions and marked wells displaying what they called cytopathic effects.

Material from those wells was harvested, subjected to plaque procedures and propagated.

The researchers then radiolabeled recovered material, separated portions through sucrose-gradient sedimentation and agarose-gel electrophoresis, extracted selected material from gels, and treated it with enzymes.

The paper describes the principal analytical method:

“Oligonucleotide fingerprinting analysis was performed by two-dimensional polyacrylamide gel electrophoresis of RNase T1-resistant oligonucleotides.”

Selected spots were then treated with RNase A and separated by DEAE paper electrophoresis for what the authors called base-composition analysis.

The direct products of those procedures were fingerprint spots, bands, migration patterns and calculated base compositions.

They were not direct observations of coronavirus genomes combining.

The Fingerprint Patterns Became ‘Recombination’

The researchers compared the fingerprint pattern attributed to a recovered preparation called B1 against patterns previously attributed to the A59 and JHM strains.

They reported that B1 contained most spots assigned to A59 but lacked several A59-associated spots.

They also reported that B1 contained spots assigned to JHM.

From those spot-pattern comparisons, they concluded:

“These results strongly indicate that B1 virus represents a true recombinant between A59 and JHM, with the crossover point roughly 3 kilobases from the 5′ end of the genome.”

The fingerprint patterns are visible on pages 2 and 4 of the paper (seen above).

The authors labeled particular spots as belonging to A59, JHM or B1, then used their presence or absence to construct the genome diagram on page 3 showing a proposed JHM-derived section attached to a largely A59-derived remainder.

The diagram was not a direct image of a combined genome.

It was a schematic interpretation constructed from the fingerprint spots and from genomic locations assigned in earlier publications.

The Paper Declared the Inference Established

The paper’s abstract did not describe the conclusion as tentative.

It stated:

“One recombinant virus, B1, was found to contain mostly A59-derived sequences, but the 3 kilobases at the 5′ end of the genomic RNA was derived from JHM.”

It then announced:

“These data represent a first report of RNA recombination between viruses, other than picornaviruses, which contain nonsegmented RNA genomes.”

The introduction was even stronger:

“These data offer unequivocal evidence that true RNA recombination can take place in viruses other than picornaviruses which contain nonsegmented RNAs.”

In the discussion, the authors declared:

“The data presented in this report clearly established that true genetic recombination occurs between nonsegmented RNAs during the replication of murine coronaviruses.”

They then extended the inferred laboratory event into a broader explanation of coronavirus development:

“RNA recombination is likely to have contributed to the diversity of coronaviruses.”

This is where a series of indirect laboratory readouts became a foundational statement about what coronaviruses supposedly do.

The Claimed Event Was Never Directly Observed

Despite declaring that “true genetic recombination” had been “clearly established,” the authors did not directly observe:

• two coronavirus genomes;

• one genome physically interacting with another;

• genetic material moving between them;

• the proposed crossover;

• or the production of a combined genome inside a cell.

The authors acknowledged:

“The mechanism of recombination is not clear at the present time.”

They then proposed a possible mechanism using explicitly speculative language:

“These RNA intermediates could conceivably bind back to the negative-stranded RNA templates of a different virus in a mixedly infected cell and generate RNA recombinants.”

“Could conceivably” describes a proposed explanation for an event that was not observed.

The instruments produced patterns from processed laboratory material.

The paper supplied the biological identities, genomic locations, crossover event, and mechanism attributed to those patterns.

Baric Was Present When the Premise Entered Coronavirology

Baric was not merely a later researcher who cited this paper.

He was its second-listed author.

The paper appeared during the period when Baric was working directly with Lai and Stohlman and helping extend the molecular framework developed through Lai’s earlier coronavirus research.

The progression is direct.

• In 1978, Lai and Stohlman interpreted sedimentation, electrophoresis, enzymatic sensitivity and binding measurements as revealing the proposed molecular properties of a coronavirus genome.

• In 1985, Lai, Baric and their co-authors interpreted fingerprint patterns and related assays as “showing” that two coronaviruses could contribute different portions of their supposed genomes to produce a new coronavirus.

• Later, Baric’s laboratory developed methods for deliberately reconstructing, modifying and assembling coronavirus genomes.

The earlier inference no longer had to be established.

It became an assumption within the research program.

From Inferred Natural Combination to Deliberate Laboratory Assembly

The importance of the 1985 paper is therefore larger than the claimed identification of one “recombinant.”

It helped establish that coronavirus genomes were modular and interchangeable.

Or, more precisely, that corresponding regions attributed to separate coronavirus genomes could be combined while producing what researchers interpreted as a functioning new coronavirus.

Later coronavirus engineering adopted the same conceptual framework deliberately.

Researchers no longer merely claimed that coronavirus genetic regions combined during an unobserved natural process.

They began selecting, replacing, mutating, and assembling the regions themselves.

The conceptual progression was:

• Indirect measurements were interpreted as defining the coronavirus genome.

• Then: Indirect fingerprint patterns were interpreted as showing that portions of two coronavirus genomes had combined.

• Then: That inferred capacity became accepted background for deliberately manipulating and assembling coronavirus genomes in the laboratory.

Bottom Line

The significance of the 1985 Lai-Baric paper extends far beyond its reported experiment.

It helped establish one of the foundational premises of modern coronavirology: that two different coronaviruses can contribute different portions of what researchers regard as their genetic material to produce a new coronavirus.

The paper did not report directly observing two coronavirus genomes physically combining.

Instead, it interpreted fingerprint patterns, electrophoretic separations, base-composition analyses, and other indirect laboratory readouts generated from processed biological material as representing that event.

The paper then declared that “true genetic recombination” had been “clearly established,” while simultaneously acknowledging that “the mechanism of recombination is not clear” and proposing only that the claimed process “could conceivably” occur.

That inferred premise did not remain confined to one paper.

It became accepted background within coronavirology.

Later researchers, including Ralph Baric, generally inherited it rather than establishing it again from direct observation, then built increasingly sophisticated methods for reconstructing, modifying, and engineering coronavirus genomes upon it.

The historical question raised by this paper is therefore larger than coronavirus engineering alone.

If foundational premises of modern coronavirus genetics entered the scientific literature through interpretation of indirect laboratory readouts rather than direct observation of the claimed biological events, and that same molecular framework later informed pandemic diagnostics, genomic surveillance, vaccine development, and many of the scientific claims underlying the government’s COVID-19 response, what does that imply about the evidentiary foundation upon which the entire framework was built?

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For decades, U.S. taxpayer-funded coronavirologist Ralph Baric developed increasingly sophisticated methods for reconstructing, modifying, and engineering coronaviruses.

Before 2019, his work claimed to explain down to the molecular level how to build coronaviruses with the defining properties (here) of the purported COVID-19 pandemic virus, SARS-CoV-2 (having six segments separated by five restriction sites and a spike protein with optimized furin cleavage sites, receptor-binding domains, and two-proline vaccine constructs).

But Baric’s work depended on an earlier generation of coronavirus research.

Before coronavirus could be said to be cloned, manipulated, or engineered, researchers first had to establish what coronavirus was understood to be at the molecular level.

One of the earliest papers attempting to answer that question was Michael M. C. Lai’s 1978 paper, RNA of Mouse Hepatitis Virus, funded by the U.S. government under grants 1F32-NS-05410 and RO1-NS-12967.

Rather than directly observing what it ultimately described as the coronavirus genome, the paper used a series of laboratory measurements to infer conclusions about coronavirus’s molecular and genetic properties.

That’s important because Baric spent much of the early 1980s co-authoring coronavirus molecular genetics papers with Lai, helping develop the molecular framework that Baric’s later engineering work would build upon.

If the molecular and genetic properties of coronavirus were originally established through interpretation of laboratory measurements rather than direct observation, how many later conclusions about the coronavirus genome ultimately rest on inherited inferences rather than direct observation?

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The Paper Began With Laboratory Measurements

According to the paper, the researchers collected biological material from treated cell cultures.

They then separated portions of that material through repeated centrifugation, chemically processed selected portions, and subjected the recovered material to multiple laboratory tests.

Those procedures produced measurements.

Not genes.

Not genomes.

Measurements.

Among them were:

• sedimentation behavior during centrifugation,

• migration patterns during electrophoresis,

• sensitivity to RNase,

• and binding in a poly(A) assay.

The instruments produced peaks, bands, enzyme reactions, and binding results.

They did not directly observe what the paper ultimately described as coronavirus genetic material.

The Paper Then Attributed Genetic Properties to Coronavirus

After presenting those laboratory measurements, the paper drew broader conclusions.

The authors concluded that coronavirus possessed:

• one large RNA molecule,

• a particular estimated molecular weight,

• a single-stranded structure,

• was “probably” positive stranded,

• and possessed the genetic coding capacity discussed throughout the paper.

Those statements are not instrument outputs.

They are the paper’s interpretation of the laboratory measurements.

In other words, the paper moved from measured laboratory behavior to published statements describing the proposed genetic properties of coronavirus.

What Else Could the Measurements Mean?

One of the most important distinctions in Lai’s paper is the difference between what the laboratory instruments directly measured and what the paper concluded those measurements meant.

The laboratory measurements did not logically require the conclusion that the recovered RNA was the coronavirus genome.

They required an additional interpretive step that attributed those measured properties to coronavirus itself.

The instruments themselves did not report:

• “coronavirus,”

• “genome,”

• “single stranded,”

• or “positive stranded.”

Instead, they produced physical laboratory observations.

• A centrifuge measured how material sedimented.

• A gel measured how recovered material migrated.

• RNase testing measured whether the recovered material was sensitive to enzymatic degradation.

• A poly(A) assay measured binding behavior.

Those observations are physical measurements.

The biological meaning of those measurements had to be inferred.

For example, a single band on a gel establishes only that a recovered material migrated as one dominant band under those experimental conditions.

• By itself, it does not establish what that material is.

Likewise, RNase sensitivity establishes only that the recovered material behaves as RNase-sensitive nucleic acid under the conditions of the assay.

• It does not, by itself, establish its biological origin.

Sedimentation behavior establishes that material sediments in a particular way.

• It does not uniquely identify the molecular identity of that material.

Poly(A) binding establishes that material interacted with the assay.

• It does not, by itself, establish that the material is a coronavirus genome or determine its strand polarity.

The paper therefore combined all of those laboratory observations and interpreted them together as evidence that coronavirus possessed a single large RNA genome with specific genetic properties.

That interpretation became the paper’s conclusion.

The measurements themselves, however, do not contain biological labels.

They describe how recovered material behaved under laboratory conditions.

The paper supplied what those behaviors meant.

Later coronavirus researchers generally inherited those published interpretations as part of the molecular framework of the discipline rather than re-establishing them from first principles.

A Molecular Framework For Later Coronavirology

The importance of Lai’s paper lies not only in its inferred conclusions.

It lies in what later coronavirus researchers like Baric did with them.

Subsequent coronavirus papers generally did not begin by re-establishing whether coronavirus possessed these proposed molecular properties.

Instead, researchers increasingly treated those conclusions as established and turned to new questions:

• How are coronavirus genes organized?

• How are they transcribed?

• How does recombination occur?

• How can coronavirus genomes be reconstructed?

• How can they be experimentally manipulated?

Over the following decades, coronavirus research shifted from characterizing coronavirus’s proposed genetic properties to studying, modifying, and ultimately engineering them.

Before Engineering Came Attribution

Ralph Baric’s laboratory did not begin by determining whether coronavirus possessed a genome with particular molecular characteristics.

His work largely assumed the molecular framework established by earlier coronavirus research.

That earlier work, including Lai’s, attempted to announce what genetic properties coronavirus possessed by interpreting laboratory measurements and publishing those interpretation-dependent conclusions as characteristics of coronavirus itself.

Only after those proposed properties became accepted within the discipline could later researchers begin organizing them, manipulating them, and eventually engineering them.

Bottom Line

Michael M. C. Lai’s 1978 paper marks an early turning point in the development of molecular coronavirology.

Rather than directly observing what it ultimately described as the coronavirus genome, the paper interpreted a series of laboratory measurements and published conclusions attributing specific molecular and genetic properties to coronavirus itself.

Over the decades that followed, later coronavirus researchers generally inherited those published interpretations rather than re-establishing them from first principles.

As the field progressed from molecular characterization to transcription, recombination, reverse genetics, infectious clones, and ultimately coronavirus engineering, the interpretive framework established by early papers like Lai’s became part of the discipline’s accepted foundation.

The broader question is not whether Lai’s conclusions were correct or incorrect.

It is how much of modern molecular coronavirology—including later claims about the coronavirus genome and COVID-19 origins—rests on direct observation, and how much rests on interpretations that became accepted as foundational over time.

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