WHEN THOMAS ERIC DUNCAN landed in the US and brought Ebola to Texas, infecting two nurses, one of whom was given then green light to board a plane even though she had been exposed, serious gaffs were made by CDC.  The nurses (in fact all health care workers) were given the wrong protection gear instructions from the CDC. Then-Director at the CDC Thomas Frieden was hauled in front of Congress, and skewered by the House Select Committee.  When Freiden testified to Congress that he would not be concerned about an outbreak occurring in the United States unless there was a mutation in this virus, and in his words, “there are none”, he was doing was CDC does best: he was putting policy before science.

There were actually 396 mutations in the virus compared to the strain isolated in 1995 from an outbreak in Zaire. I was actually analyzing them during the misleading hearings. The virus from 2014 was only 97.3% similar to the strain from Zaire, not “99.9999%” similar, as a CDC scientist incorrectly reported on a White House conference call in which scientists from the US were told to represent Ebola facts the way that the CDC wanted us to.  That was just before the White House asked the AP to stop reporting on suspected cases of Ebola in the US.  (All of these events, and the science of Ebola, are chronicled in “Ebola: An Evolving Story“.

One of those mutations, we now know, drove the infectivity of Ebola in an insidious manner.

The A82V Mutation

At the time, as Director of the Ebola Rapid Assay Consortium, I pressed, in every venue, in national meetings, and WHO conference calls, PCR tests might be missing infected individuals due to mutations, and that epidemiologists concerned about the rates of mutations were far off the mark [Read “Ebola Evolving: It’s Not the Rate, It’s the Mutation”].  Important biological changes occur in evolution only rarely, with most mutations not bringing about significant biological differences.  The progression of the spread of Ebola in 2014 was prolonged, involving as large number of human-to-human transmission events.  Under these circumstances, divergent evolution is guaranteed.  Each infection would lead to trillions of new Ebola viruses, and each transmission involved a genetic bottleneck, sampling a subset.  Both in the race to infect available cells in the host, and in chance opportunities for infection, Ebola viruses with meaningful genetic differences undergo competition.  Under these circumstances, the probability of adaptation to the host species (in this case, humans) was high.

The initial transmission of a virus from an animal host to humans is a change in the environment for the virus. Phylogenetic analyses conducted at the time showed co-circulation of several different types during the outbreak. Evolution needs three things to occur: (1) meaningful genetic variation that exists among individuals tied to survival and reproduction, and (2) time. The prolonged human circulation certainly led to an accumulation not only of differences, but to genetic variation, which drives adaptive evolution.

The GP-A82V mutation is non-synonymous mutation located at the NPC1-binding site Ebola glycoprotein (GP) mutant A82V. Analyses showed that the mutation existed early in the 2014 outbreak, and increased in frequency over time.  A study found that GP-A82V has heightened ability to infect primate cells. Cells that do not have the primate-specific NPC1 sequences at the EBOV interface are not as easily infected.  This supports that the A82V mutation is an adaptation to the human host.  Among infected individuals, GP-A82V was associated with increased mortality: the increased infectivity included human dendritic cells.  It’s almost as though the GP-A82V strain specifically disabled the human immune system.

Another study (Urbanowicz et al., 2016) confirmed these findings. Researchers generated a synthetic glycoproteins that represented the various lineages that circulated during the outbreak and put them into pseudoviruses.  These showed variation in their ability to infect various human and bat cell lines. GP-A82V had higher infectivity in human cells, and reduce infectivity for bat cells.


This is the phylogenetic tree (derived by the authors of the study) using data from A. Rambaut’s collection of Ebola genome sequences.  Important findings of both of these study include that adaptive mutation leading to this important amino acid substitution occurred early in the tree, and just prior to the exponential increase in the rates of transmission, and then went on to outpace the original genetic variant.  This means (to me anyway) that genomic surveillance early an outbreak should include infectivity assays using pseudoviruses so the most threatening types of Ebola (or other pathogens) can be most aggressively pursued.These two studies strongly support that the GP-A82V drove increased rates of transmission among people in West Africa.

A third study found two additional mutations, one of which, a D759G substitution in the active center of the L polymerase, increased viral transcription and replication (Dietzel et al, 2017).   They found another variant that led to decreased decreased viral transcription and replication.

Clearly, the specific mutations, not the rates, matter.

The lessons?

(1) During outbreaks, don’t focus on mutation rates.  Focus on transmission and mortality rates first. Those are phenotypes. With equal priority, focus on non-synonymous substitutions and perform functional assay analyses of those predicted (via computer modeling) to most likely effect protein structure.  Pour money into stopping the transmission of those suspected of being most transmissible, and have a field test available to determine, for each patient, the viral genome sequence.

(2) Don’t lie to scientists and to the public, and always put science before policy.  Every time.  Every pathogen.  Every vaccine.  Misinformation from the CDC prevented many scientists from looking further into available data on Ebola at time when EVERY scientist should have been looking, and such misinformation continues to prevent most scientists from knowing the reality of numerous out-of-date vaccines on the CDC schedule.

Such as this 2014 report that shows that most individuals who receive a whooping cough diagnosis are vaccinated.

Most scientists do not know it yet, but Tdap is a failed vaccine (see Epidemic Pertussis and Acellular Pertussis Vaccine Failure in the 21st Century) that has been expanded for use during pregnancy with insufficient safety data, and the CDC has not produce any data on fetal mortalities associated with the use of Tdap during pregnancy.  We need a new pertussis vaccine that excludes unsafe epitopes which are too similar to human proteins.  And we need randomized clinical trials that are sufficiently powered to detect adverse events with long-term follow up of total health outcomes awareness.  And they need to be conducted by research teams with no conflicts of interest. This Slate article shows how adverse events that might be attributable to HPV vaccine were excluded from consideration by Merck in data submitted to the FDA.  And two whistleblowers who claim that the MMR efficacy data was fudged by adding anti-mumps virus antibodies to fool the FDA into thinking the MMR was highly effective against mumps will shed more light in 2018 of the effects of putting profit and contracts before science.

And while we are considering misinformation from CDC, flawed policies matter, too.  Why is the flu vaccine only 10% effective, and how in the world can CDC recommend its use based on a hope for herd immunity?

Every scientist needs to know that thimerosal is in flu vaccines – some of them – and that thimerosal specifically inhibits ERAP1.  They need to know that CDC recommended flu shots w/thimerosal for pregnant women – preferentially.  Thimerosal not only contains ethyl mercury, which can induce neurodevelopmental disorders.  It also targets the human immune system protein that shortens proteins ERAP1-deficient cells have reduced surface levels of MHC class I molecules, and the peptide-MHC complexes that are made are less stable than on wild type cells – meaning thimerosal will make you more susceptible to infection from pathogens to which you are already immune.  This likely explains why people who get the flu shot are more likely to experience respiratory infection from non-influenza viruses.  And why getting the flu shot last year makes next year’s flu shot less effective.  So women who get the flu shot while pregnant may be more likely to experience high fevers due to infections. Maternal immune activation is dangerous.

My wishes for 2018: Scientists – bona fide, objective scientists, who in every other consideration of fraud conducted by Pharma on drugs will sign on, a swear that Pharma is corrupt, but who cannot allow themselves to transfer that perspective and when it comes to vaccines – to at least stop blaming the antivaxxers.  Vaccine fatigue is real. People with whooping cough and mumps are most likely vaccinated, and can become asymptomatic carriers. Vaccine safety science fraud is real.

And don’t blame mothers of vaccine-injured children for warning the world.  They are the most caring people on the planet.  They vaccinated.  Their child was injured.  The world refused to acknowledge it as an injury.  And yet they persist.  They continue to help raise vaccine risk awareness. And they won’t stop until vaccine risk is minimized.

We need to do much better in 2018. Because, if we are going to put science before policy, we have to do science.

I’ll be conducting research on ways to help kids learn to speak.  Because we want to know what all kids have to say. And on vaccine risk screening biomarkers to prevent vaccine injuries. Because vaccine injury is real, pervasive, and routinely neglected. And I’ll be facilitating and enabling research on safe ways of removing aluminum from our brains. Because amyloid in the brain is part aluminum (Nikaido et al., 1972; Masters et al., 1985; Yumoto et al., 2009).



Diehl, WE et al., 2016. Ebola virus glycoprotein with increased infectivity dominated the 2013–2016 epidemic Cell. 167(4): 1088–1098.e6.

Dietzel E, et al. 2017. Functional Characterization of Adaptive Mutations during the West African Ebola Virus Outbreak. J Virol. 2017 Jan 3;91(2). pii: e01913-16. doi: 10.1128/JVI.01913-16.

Masters CL et al., 1985. Neuronal origin of a cerebral amyloid: neurofibrillary tangles of Alzheimer’s disease contain the same protein as the amyloid of plaque cores and blood vessels. EMBO J. 4:2757-63.

Nikaido T et al., 1972. Studies in ageing of the brain. II. Microchemical analyses of the nervous system in Alzheimer patients. Arch Neurol. 27:549-54.

Urbanowicz, RA et al. 2016. Human Adaptation of Ebola Virus during the West African Outbreak Cell. 167(4): 1079–1087.e5.

Yumoto S et al., 2009. Demonstration of aluminum in amyloid fibers in the cores of senile plaques in the brains of patients with Alzheimer’s disease. J Inorg Biochem. 103(11):1579-84. doi: 10.1016/j.jinorgbio.2009.07.023.

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