Ebola Two Years On: Mistakes We Must Not Repeat

Ebola Two Years On: Mistakes We Must Not Repeat

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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An Epiphanic Epitaph for the Coming New Reality of Science


Science sits on a threshold of a new reality, and while not begging special knowledge, the majority of scientists have seen it coming but only now are able to allow themselves to consider it.

Science is embarking on this new reality, as occurs in so many revolutions, by casting off the illusion of its masters. Perceptions of control warp the mind, and struggles for reconciliation, and pleas for acceptance are neither harmless to knowledge, nor our ability to perceive it. There is an alpha in the process of science, which begins with admission of ignorance, polluted by specks of presumed knowledge, sometimes tainted with hubris. And the structure of Logos then proceeds, like a broken algorithm, in fits and starts, toward an approximation of a process we amusingly in retrospect call discovery. The Dominants in science seek validation, through quantitative measures of their own success, of their own primacy. Both the measures and their social primacy eventually show themselves, to be both temporary and false. The Betas seek to find, in this hot mess of Science, some validation of understanding, which has a longer run, but which is also eventually replaced, sometimes for the better, sometimes for the worse. Many feel their way through science, which is incorrect, for rational discourse demands logic; however, it is also a myth that feelings have no place in science. The scientist in control of their faculties use reason to decide what things to have feelings about, but not necessarily how to feel about those things.  The scientist who gives that control to others has sold the tools they need to thrive, like a painter selling their brushes, or a sculptor selling their tools.

There is only but one nemesis of knowledge and that is ignorance, which itself is not always innocent; at time it is foisted upon the public when the processes of science pose perceived threats to a paltry coherence that holds some in power, which itself is also a placeholder in the longer time frame of history. An ethos for The New Science is to not seek the lost civilization, nor the forgotten knowledge in eons long past, except as a child pondering an amusement park. Seek instead what we are losing each day in the exponential orgasm of information production without reflection; seek what is lost by ignoring the moments between; seek to know what others know and credit them profusely for the grains of truth they stumble upon. Seek to hold on to what will be forgotten and lost to time if we fail to recognize it. Use the most robust tools, the most powerful designs and tests for deducing from without, not inducing from within, the next slippery sliver of something that might be knowledge. And when all else fails, do not settle for merely adding or pruning leaves from the Tree of Knowledge.  Instead, shake it at the base, question its assumptions without hesitation or apology, explore the what-if possibilities of alternative growth patterns and run the simulations in your mind on possibilities if past false assumptions and conclusion deep in the heart of the wood had been, at THAT time, recognized to be incorrect, or incomplete.  Because rest assured that many, if not most, will be discovered to be wanting, perhaps in our equally conceited now, or in some distant objective, reflective, demure and retiring future foolishly satisfied with its own imperceptible foibles.  When rot sets in, it is necessary, and the duty of every scientist, to dig the tree of knowledge up, to examine its roots for the healthiest parts, and then to plant and nurture those for a new beginning, which begins at the start of the story, not only at the end.  And most of all – now this is important – become and remain an activist for objectivity, reason, deduction, and passionate impartiality. That in and of itself is a goal, not a prescription.

If, in all of this endeavor, you manage to make some money while generating knowledge, more power to you.  But remember two things:

(1) money should be side-effect of a robust science, and, if you are “successful”

(2) use all three (money, knowledge, and power) wisely.

-James Lyons-Weiler, PhD

Allison Park, PA

May 2, 2017

The Evolutionary Arms Race Theory of Autoimmune and Pathogen-Derived Disease

WHEN EVOLUTIONARY EXPLANATIONS are invoked to explain the existence or persistence of specific traits, the normative causal reasoning invokes a benefit to the individuals carrying the genes for those traits, a concept widely understood as individual fitness. Natural selection then results as an outcome of competition for the most limiting resources; nature providing a grand stage upon which all of the theatrical plays of life are worked out, with some characters thriving, and others denied contribution to the next generation: for them, their genetic fate a dismal tragedy.

Tomes of very clever ideas, and deep understanding on how complex evolution could become were worked out in for 90 years in the pages of journals that published theoretic works by such great minds as Sewall Wright, whose ideas on balancing selection explain how two traits, both slightly deleterious, could offset a certain march toward genetic doom, leading to the persistence of apparently deleterious traits in evolving populations over time. He and others worked out ways of thinking about complex genetic interactions, such as epistasis and pleiotropy. All shifts in gene frequencies in most populations, it was thought, could be well understood as a balance between the relative contribution of alleles to benefits in terms of survival and reproduction, and inconsistencies and unlikely or seemingly impossible outcomes could result from shifting balances across adaptive landscapes.

Utterly brilliant, this body of work was confronted with new thinking upon the the arrival of even the earliest modicum of data. Motoo Kimura showed, for example, that most of the changes in gene frequencies over time where not likely to involve a specific genetic contribution to differential survival and contribution, but were rather simply a mathematical necessity, driven by mutation pressure, with most nucleotide substitutions occur simply because there was not enough room in the population to hold all of the alleles at a particular locus.  This is most easily understood in very small populations, in which the smallness of the population makes random factors that influence the survival of specific genetic variants much more important than they would be were those alleles found in a larger population.  Alleles could come into existence by chance, and then, driven by chance, drift to fixation to the exclusion of other alleles, regardless of their relative contribution to survival and reproduction. Under Kimura’s neutral theory of evolution, and its descendant theories, mutation pressure places the limit on the rate of evolution, and most (the largest percentage) of genetic changes over time do not particularly influence survival or reproduction, or if they do, those changes are carried along by chance.

We should understand that evolution is a combination of Darwinian natural selection and neutral drift, both rates limited by the amount of genetic variation in a population, which itself is limited by factors such as mutation rates, and, for a given subpopulation, access to mates from other subpopulations that might contain different alleles and genotypes.

We understood then, and now, that mutations are not restricted to nucleotide substitutions, and that large, deleterious genomic differences can have large impacts on the relative survival and reproduction of individuals, and that many traits observable in a population are impacted by the effects of the environment on specific methylation patterns in parents’ (mostly mothers’) chromosomes, and that changes that occur during gametogenesis both of the nucleotide, genomic and epigenetic levels can be found in offspring.  Most of the important changes in phenotypes over time likely involve traits that are related to differential survival and reproduction, both of which can be measured objectively. In other words, what is important in terms of providing an evolutionary explanation can be consider both in terms of counting shifts in gene frequencies, and in their impact on survival and reproduction.

When I began studying genomic and genetic changes related to disease, I performed most of my studies in the service of others.  I worked hard to try to insure that the data analysis techniques and models we employed were at least empirically reproducible, if not founded upon a solid theoretical basis. When technologies brought the ability to assay entire genomes, transcriptomes, and proteomes, I worked overtime, often deep into the early morning hours, comparing alternative methods for data representation, normalization, finding differences, and interpreting in search of the most empirically justified frameworks to tell, from a single data set, which gene, proteins, or methylation patterns were likely to be truly important to a particular disease. I started in cancer (nearly all types), and from there moved into nearly every other domain in medicine, including immunology, diabetes, pulmonology, neurology, and so on.  In any given week I’d be involved in 4-5 different studies, and when finally tallied, during my 16 years working exclusively on these problems, I contributed results to over 100 studies. Many of the studies were, appropriately, at the pilot level.

I had on several occasions had the opportunity to work with experts in immunology, working on cancer. There was a good reason for that. Part of my attraction to come work at the University of Pittsburgh Cancer Institute was Dr. Ronald Herberman, who had assembled a cracker jack team of immunologists focused on various aspects of cancer. While working as a post-doc under Dr. Masatoshi Nei, I became immersed in his excellent studies on the evolution of the very complex MHC loci, and the amazing adaptive immune system, which uses combinatoric guessing at matching epitopes found in nature.  While at Penn State and at UPCI, it took a while, but over time I became intimately familiar with the roles of the various types of cells, tissues, and signals. It was an amazing time, and I had never been happier in my position.  From breast cancer to melanoma, pancreatic cancer and lung cancer, I had a chance to see some of the world’s foremost researchers working to understand both the causes of and processes of cancer, driven by mutations, and the optimal routes to treatment. Biomarkers were a huge part of the experience, as well as a search for their biological significance (function).

My interests in autoimmune disorders sank in while writing three books: Ebola: An Evolving Story; Cures vs. Profits:Successes in Translational Research, and The Environmental and Genetic Causes of Autism.  The experience of writing those three books allowed me to go into deep study of the primary literature on disease causality specifically on factors that impact our immune systems.  Factors that most profoundly influenced my understanding that leads to this Evolutionary Theory of Autoimmunity of course were threefold:

(1) intensive study on the role of cytokine storms in the pathogenesis caused by Ebola infection, specifically the devastating positive feedback loop caused by our immune systems’ responses to the effects of Ebola on our tissues, with releases of cytokines that activate escalating responses to cellular damage

(2) the theoretical and empirical basis for artificial immunization, and its attendant consequences (most often attempted using vaccines), and

(3) being inundated with information on the fact that dozens and dozens of conditions known as autoimmune disorders defy explanation.

Readers of “Causes” will come to understand that autism can often involve a form of ‘innate’ autoimmunity, in which specialized cells in the brain that play the role of scavenger of cellular debris and killers of intruders, like white blood cells, can become chronically activated in the presence of the persistent activating signal of the excitotoxic amino acid glutamate.  Ironically, I cannot consume monosodium glutamate, because it brings an onset of migraine headaches. This same amino acid, in some kids’ brains, stays at high levels and rather than ceasing their destruction, say, of unhealthy brain tissue, they go on the attack and destroy both dendrites and neural precursor cells, which release cytokines signalling cellular damage, injury, and death, causing sustained microglial activation. The original cause of the high glutamate levels in the brain of autistics may vary from case to case; it may involve a mutation in a glutamate receptor found on astrocytes, or it could be environmental damage to astrocytes via metals, which preferentially bind to astrocytes, and which have been shown to localize both to the nuclear pore and the cell membrane  The literature on traumatic brain injury and stroke is now rapidly filling with how helpful it is to keep microglial cells quiet before they set off a positive feedback of cellular destruction that keeps them chronically activated.

They will also come to understand that many of the adverse events seen in vaccines, and autoimmune disorders, which in most doctor’s minds have root causes that defy explanation, are in fact induced by the presentation of foreign antigens into the human in the presence of an adjuvant. Dr. Yehuda Schoenfeld presented this idea in 2010:

Shoenfeld Y, Agmon-Levin N. 2010. ‘ASIA’ – autoimmune/inflammatory syndrome induced by adjuvants. J Autoimmun. 2011 Feb;36(1):4-8. doi: 10.1016/j.jaut.2010.07.003.

Other studies by Schoenfeld and colleagues have found that ASIA has been reported from nearly every vaccine on the market, and that we may be able to predict who is at highest risk of developing autoimmunity from after vaccines:

Soriano A, Nesher G, Shoenfeld Y. 2015. Predicting post-vaccination autoimmunity: who might be at risk? Pharmacol Res. 2015 Feb;92:18-22. doi: 10.1016/j.phrs.2014.08.002. Epub 2014 Sep 30.

I suspect that in time most autoimmune disorders, which are known to involve adaptive immune systems attacking healthy human tissue, will be seen to ultimately have an evolutionary explanation. Part of it will seen an resulting from adaptive advantage to pathogens in their ability to effect their hosts in a manner in which transmission to new hosts is made more likely. While none of which could possibly be seen as resulting from adaptive advantage to humans, we will explore that in detail later. It must also be remembered that a part of the explanation could also be come to be seen to be the result of mere chance.

MULTIPLE SCLEROSIS is a devastating progressively degenerative disease that, at its heart, involves stripping of axonal sheaths from victims, leaving their nerve cells in the brain and the spinal cord unable to transmit signal due to a lack of insulation and improper ionic balancing associated with nerve impulses. Clearly, whatever has caused demyelination, and demyelination itself, cannot be seen in Darwinian terms to be adaptive to humans. This is about as far as evolutionary thinking has applied.  The dismissal of an evolutionary explanation based on the ideas that genes that encode ‘for autoimmunity’ would be quickly removed from a population is only halfway thought through. The more rapidly evolving organism on the scene, of course, is the pathogen itself.

From a pathogen’s perspective, for example, making its host immobile, especially if that host is a member of a social species, will increase the duration and frequency of contact between the affected and the unaffected, leading to increased transmission of the pathogen. Thus, from a pathogen’s fitness perspective, injuring its host, without killing it, would be adaptive. The pathogen itself could do the damage, such as infecting specific types of brain cells and impairing movement. Or, it could have the same effect by turning the hosts’ immune system against itself. We could expect that surface epitopes seen on viral proteins would be on adaptive landscape that could lead them, as a result of chance mutation after mutation, to become increasingly similar to key host proteins. In fact, many pathogens that infect humans have been infecting other primates for millenia, and the common, successful ones causing hardly any or no symptoms at all in their native hosts. But many pathogens do cause disease in their native hosts, including paralysis and death.

Within the human population, then, we could see that whichever proteins that are attacked by the immune system as a result of the development of cross-reactive antibodies would be on an adaptive landscape upon which there would be advantage to moving, random mutation by random mutation, further away from the offending pathogen’s epitope sequence.

We can make predictions on the basis of this theory: genes that encode spefic surface epitopes on pathogens that cause autoimmune disorders should show high rates of non-synonymous substitutions that cause increased conformational similarity to that of their hosts’ matching protein sequence, and hosts should be observed to show high rates of non-synonymous substitutions, as the run away from the offending pathogens’ epitope sequence and structure. A third prediction would be that the hosts’ proteins are much more constrained than the pathogens; they are, after all, already serving an essential function in the host, whereas the surface antigens on a pathogen may be more evolutionarily labile, freer to explore a wider area on its evolutionary adaptive landscape. (For more ideas on the importance of individual mutations vs. rates, see this article).

Adaptive Landscape 1 tif to jog

The first step toward testing this evolutionary arms’ race theory of autoimmunity is to determine which pathogens hold the best-matching epitopes.  The second step is to confirm that cross-reactive antibodies exist in autoimmune patients to specific epitopes of interest. This step also happens to be potentially very useful to patients both in terms of helping their doctors understand their disease, and could lead to individualized treatment via immunomodulation therapies. The third step is to measure, whenever possible, the rates of synonymous and non-synonymous substitutions over long enough periods of time to catch the pathogens’ protein racing toward the human sequence, and the matching human protein racing away from the pathogen sequence.

There are attendant logical predictions about variation in the rates of specific autoimmune disorders attributable to specific pathogens across human populations if genetic variation exists within the self-targeted proteins involved in autoimmune disorders.

We have begun these steps, pathogen by pathogen, autoimmune disease by autoimmune disease, at the Institute for Pure and Applied Knowledge, importantly beginning with the proteins that are already known to be targeted by our immune system during autoimmune disease.  Our findings thus far are quite promising, and may lead truly useful information, such as the knowledge of which pathogen’s epitopes should excluded from vaccine, for fear of inducing autoimmunity in a preventable manner.

One realization that we have had is that it is possible that the mechanism of pathogenesis, that is, the ways in which communicable pathogens cause disease in humans (and animals), may be precisely they same way they cause autoimmune disease. From the mildest sniffle, or the most raging fever, it seems likely that pathogen/host protein similarities, driven mostly by adaptive evolution in the pathogen, may explain most – if not all – of the symptoms by which we diagnose communicable diseases.  That would be very good to know.

My next stop in this journey will be the literature – the massive scientific literature on host/pathogen interactions – to see what is already known on this fascinating topic.  You can support our efforts at ipaknowledge.org.

-James Lyons-Weiler, PhD

October 31, 2016

I thank Celeste McGovern at Greenmedinfo for her well-written and informative post,

Attacking Ourselves: Top Doctors Reveal Vaccines Turn Our Immune System Against Us 




Ebola Evolving: It’s Not the Rate of Evolution, It’s the Substitutions

A NUMBER OF STUDIES have reported results from the analysis of the genomic sequences of Ebola, acquired from patients in the 2014-2015 outbreak/epidemic.  The authors suggest that because evolutionary rates have not increased relative to those observed in past outbreak of Ebola, the virus has not and is not becoming more pathogenic.  They conclude that there is not, as originally reported [4] an increase in the overall mutation rate. Therefore, the presumption is, it is not acquiring new characteristics that would make it something more likely to cause death and wreak havoc on societies.

These reports also reassure us that because there are few non-synonymous substitutions relative to synonymous substitutions, natural selection is not likely to be necessary to invoke as a factor.  I.e., again, the virus is not ‘adapting’, and, therefore, because it is not becoming more fit to infect and spread among us, we can relax.

The press dutifully reports, and extends the conclusion, to reassure use that, for example, Ebola is therefore not likely to “go airborne” [5,6,7,8]. [See Ebola: An Evolving Story to find out why that is not the most interesting question to ask about Ebola evolution in the first place.]

As a life-long student of evolution, endowed with a PhD in Evolution (among other things), I understand where they are coming from. That is, I understand what they are talking about. Before jumping into biomedical genomics and genetics, I enjoyed two years in a Post-Doctoral position with Dr. Masatoshi Nei at Penn State University.

However, based on my understanding of evolutionary processes, I have no idea why they come to such conclusions looking at mutations, mutation rates, rates of nonsynonymous/synonymous substitutions. Here are my observations on the matter:

(1) It will very well understood that the limit on the rate of evolution – i.e., the rate at which gene frequencies can change over a specified (long) period of time – comes from the mutation rate.  Dr. Motoo Kimura demonstrated this elegantly with mathematics with which every card-carrying evolutionary biologist should be familiar. Therefore, unless some factor has led to an increase in the error rate of polymerase, DNA repair enzymes, we should not expect an increase in the observed overall mutation rate (influx).  This virus hijacks, of course, our polymerases and post-replication mismatch repair enzymes. Thus, unless our polymerases and DNA repair enzymes have mutated in some way, we cannot expected increases in mutation rates.

(2) Selection removes variation, and adaptation does not always “increase” a phenotype.  To the extent that mutation rates and evolutionary rates themselves are adaptive, a slow-down in the overall mutation or evolutionary rate could easily be adaptive in the right environmental context.

(3) Looking at NonSynonymous and Synonymous substitution rates is, generally, good way to measure positive Darwinian selection over long periods of time, to identify which genes may have been influenced by positive selection (that which drives rare alleles to fixation) .  However, some mutations (such as the variant found in position 10,218 in the original Gire et al study) may not encode an amino acid at all, and could influence phenotypes other than those expressed via proteins. Viral phenotypes include replication rates, and Ebolavirus genome functioning including RNA expression includes some unusual features, such as ‘stuttering’. The GP gene encodes two “genes”, the large, GP gene, and a small sGP gene that is expressed at high ratio to the GP gene.  The sGP protein is small and can confuse with misleading antigenic signal, or overwhelm the immune system with sheer numbers.

(3) Random gene frequency fluctuations occur in populations of small size. Ebola has anything but small population sizes. In my book “Ebola: An Evolving Story”, it is explained that new virus particles infecting a human (or gorilla, chimp, or bat) is akin to a lifeform colonizing a new planet.  In the few short (3-4) first days of infection, viremia can rise from nothing to billions of copies per mL of blood. Thus, random fluctuations, and random fixations, are not likely to occur.  It is very difficult to move gene frequencies around by chance in large populations, and the selection coefficient must be immense for alleles to march from next to nothing to fixation.  “What about the repeated genetic bottlenecks?” you might wonder, speculating that transmission usually involves perhaps a few viral particles. Putting aside the reality (it varies – a lot), let’s assume each transmission involves a handful of virus particles in the hot feces or vomit that someone inadvertently is exposed to.  In the growth phase of the epidemic, the sampling process, if done by chance, occurs repeatedly and independently in different branches of the expanding population.  However, the sampling frequency of alleles, if by chance (and that’s important, first assume equiprobable transmission of all viral particle, regardless of genotype) will still be based on their standing frequency within a person at the time of transmission. Each person has hundreds to thousands of quasi-species, most at low frequencies, due to the introduction of new variants via mutations. The new quasispecies that contain new variants will be, independent of selection, at low frequency (initially 1/N). Variants that happen to occur at the beginning of an infection have a much higher likelihood of drifting to fixation by chance (drift); but none of them have the starting advantage of the founding quasispecies (which at the time of the new mutation will be m/N). Any given quasispecies at any given time will have a frequency of n/N. typically, m/N will be nearly 1.0.  Therefore, the probability that the primary quasispecies will transfer will be nearly 1.0

In a growing population, drift is rare; resources (us) are abundant, and positive Darwinian selection can be expected to be rare. It’s when resources become scarce – all of the available tissues are infected, all of the people are immune or sick and dying, or dead) when competition between genotypes becomes most intense.  However, if there are any mutations at the onset of a zoonotic transfer that gives even a rare quasispecies a strong advantages in terms of faster rates of transmission (higher RO), faster rates of replication within a host, more optimal (fast or slower, depending on the fitness function) replication rates, which tissues it infects earliest in the progress of the diseases… there are myriad ways other than going ‘airborne’ for a virus to become more nasty (more virulent, more infectious, or even less virulent if it means it will infect more individuals).

Many high-ranking officials will report that there is “no evidence” of any change in transmission mechanism. This position ignores the often reported shift in the symptomological spectrum of Ebola this time around compared to 1995. Reliable data on symptoms may be hard to come by, but most reports this time around noted much, much more severe, sudden onset (without warning) vomiting and diarrhea compared to 1995. Let’s for a moment say that the plethora of reports of shock at the rate of spread, the large percentage of health care worker deaths, and the sudden symptoms are not reliable.  Which side of a debate where one claims “neutral evolution” and the other claims “adaptive, Darwinian selection” is going to be able to demonstrate, convincingly, from genomic sequence analysis alone, that their position is the correct one?

Evolutionary biologists studying sequences need to be careful, and the press needs to be especially careful, when considering the interpretation of the analysis of sequence data. In particular, they need to pay attention to precisely which sequences from the epidemic are being analyzed, where they were collected from, and, importantly, when in the outbreak or epidemic they were collected.

The first data [1] out came from the first passages (transmissions) in the outbreak. This period of evolution is distinct from other periods of time;  pre-transfer to human, the primary quasispecies resident in the host would have been sampled at a rate of m/N, and any minor quasispecies would have been sampled at a n/N.  As the virus passed from person to person, the sampling rate of m/N would continue to predominate, unless or until a rare quasispecies became the primary in a person (either via drift or selection).

Within each person, the burgeoning population of quasispecies would typically continue to reflect a mix of the sampling of 1, 2 or maybe 3 quasispecies, and any new quasispecies that evolved via mutation while the trillion-sized population grew. With each new infection, the sampling game would continue, person after person, at a rate of {m/N, n1/N, n2/N, n3/N…no/N}, with the outcome of each sampling effort leading to a new constellation of m’s and n’s in their relative ranking.

A key question is that whether repeated bottleneck driven genetic drift, or via Darwinian selection, is behind the observed change in the frequencies of quasispecies and alleles over time. Given the billions of copies of Ebola in infected persons’ bodies at the time of transmission, genetic bottleneck is very, very unlikely to drive a rare mutation (such as the variant at non-coding position  10,218) to fixation in a serial passage situation.

In that first study, the novel allele at position 10,218 was observed, in this supposedly random, haphazard sampling game of allele frequencies, to march, seemingly deterministically, against all odds, toward fixations as the disease coursed through 96 people. This allele, a non-synonymous substitution is of unknown functional significance.  The allele was original found in 12 people, and was observed to increase in frequency to become fixed (the only variant) in 38, one of two variants in 12 patients, and absent in 28). The allele was found in another analysis to separate a fast-spreading Clade 3  from a slow-spreading Clade 2 (See the L. Bedford’s Lab “Is Ebola Adapting?”).  The variant increased over time, and was observed to cluster geographically and in the transmission chain.

A very recent paper (Park et al., 2015) analyzing more sequences mentions variant 10,218 and the third clade, which they describe a more complete picture of 10,218 as SL3, as follows (emphasis mine):

“A third lineage (SL3), derived from SL2, emerged in mid-June 2014. SL3 differs from SL2 by a single mutation at position 10,218, first found as an intrahost variant (polymorphism within one individual) at a low frequency. SL3 became the most prevalent lineage in Sierra Leone during the first 3 weeks of the outbreak there, with SL1 disappearing soon after the appearance of SL3. The SL3-defining mutation is epidemiologically important, as it is the first commonly circulating mutation observed to arise within Sierra Leone’s borders.

As the epidemic developed within Sierra Leone, the SL3 lineage continued to dominate the viral population within the country, with no evidence for additional imported EBOV lineages. In our data set, 97% of the genomes carry the SL3 mutation and the remainder belong to SL2.

Any allele that has gone from new to 97% of all genomes in the face of repeated bottlenecks during transmission is very likely adaptive. This constantly increasing frequency of all allele over time, and its association with a greater rate of spread, during the supposedly random sampling series is consistent, probabilistically speaking, with natural selection, and is by far much less consistent with neutral evolution via drift. The key here is to recall that we are talking about repeated bottlenecks from very large population sizes, and that it is very, very difficult to move gene frequencies in large population sizes.  Repeated bottlenecks can be effective at fixing common alleles, but the effectiveness of drift at fixing each allele in the founder population is equal that allele’s frequency in the parent population.  So, on average, in the Ebola scenario. we should not see nearly perfectly linear increases in a rare allele frequency toward fixation unless there is something driving it. Studies of the functional significance of 10,218 should be undertaken.

Just like the focus on ‘airborne’ as a mode of transmission detracts from consideration of other evolutionary avenues, the focus on the overall evolutionary rate confuses that fact that selection will tend to increase the rate of turnover of rare frequency alleles into high frequency, potentially fixed alleles, but not (necessarily) rate at which the total number of substitutions observed might occur. When a zoonotic transfer takes place (aka, “spillover’ a la David Quamm}, the alleles may in fact quickly sort themselves out based on the contribution of each allele to each viral particles’ within host fitness (so-called antigenic pressure, competition among quasispecies, etc.). The relative time frame can be counted in terms of passages. It can take a surprisingly low number of passages to see a virus adapt to the new host. However, once this initial adjustment to a new environment is made, the virus might then be expected to settle in, and thus would could expect that overall turnover rate or genetic flux rate might slow back down. In transferring to humans, the virus has landed on what Sewall Wright

Adaptive Landscape 1 tif to jogcalled an “Adaptive Landscape”, with various fitness peaks to travel up, reflected by the environment. If the mutation of interest (10,218) causes Ebola to cause enteric disease at an earlier stage of the disease than other hemorrhaging, and other mutations cause a prolonged incubation period before any symptoms, Ebola could spread faster either by making people either less sick, or sick in a slightly different manner.

Evolution of Virulence and Infectivity, or, more Precisely, Evolution of Pathogenicity and Morbidity

One such view on the adaptive landscape become apparent when one simultaneously considers the evolution of pathogenicity (the ability of the virus to make a person sick) and morbidity (the ability of the virus to cause diseases and deaths in a population).  Thinking of the rate of transmission, risk of infection, and the ability of the virus to travel from person to person, we can see that any virus with high pathogenicity in an person would quickly die out, killing a few members of a host species, and taking itself with them. Thus, the expected adaptation in Ebola would be toward less pathogenicity, not more. The influence of a less pathogenic virus could mean that it could survive longer, infecting more people, and still be relatively pathogenic, and could kill many, many more people.  So the news that there was a first a rapid rate of evolution, followed by a slow down, are hallmarks of rapid adaption to the virus “learning” how to persist in our species via selection.

In fact, it was the later studies that showed this. The press takes the later studies to mean that the initial study was wrong.The latter studies that show a slow-down do not impeach the previous results showing rapid adaptive evolution.  They augment them.

And the news that monkeys infected with ebolavirus from 2015 become sick 2-3 days later than those infected with the 1976 virus is not necessarily good news either, especially if there is even a small amount of pre-symptomatic transmission, or people live longer with a slower-progressing disease, shedding virus for many more days than in 1976. (See Are Data from Ebola Studies Still Being Misinterpreted?)

It’s the Substitution, not the Rate

Moreover, a mere handful of mutations could cause these phenotypic changes. With an virus as complex as Ebola (for all of its seven known genes, each with more than one known function), adaptive evolution could involve as low as a single substitution. It has occurred it me that the one allele that allows one of the virus’ proteins to fold in a manner that causes it to functional as an analgesic, or a painkiller, causing a drop in the stomach pain prior to vomiting or having diarrhea. Perhaps a second mutation would make the viral more enteric and less hemorrhagic.

That is not much molecular change.

So, a more appropriate tagline in the press would be “Scientists find evidence of possible evolution of higher morbidity in Ebola via increased transmission dues to lowered  pathogenicity” would help understand the possible risks of attendant to our species due to evolution in Ebola since the onset of the epidemic. It’s probably a good thing I’m not in charge of press headlines.

The problem is that the analysis of molecular data, even this very thorough analysis of the possible effects of the observed mutations on protein structures, do not analyze any relevant disease phenotypic data.  The Bedford lab at least examined the rate of spread. These questions are better addressed using infection experiments with animals. If we want to know if there are changes in the symptoms, we should examined the progression of the disease in monkeys with serially times autopsies to see the order in which tissues show inflammation and signs of hemorrhaging for the 1976, 1995 and the 2014 viruses.

[Addendum 7/7/2015: Concern over the phenotypic differences in the 2014 Ebolavirus has penetrated higher levels of organizations like the CDC and the NIAID where descriptions now include statements such as “we understand most of the phenotype”. My own, and others’ expressed concerns over the effects of individual substitutions on the accuracy of PCR and immunohistochemistry-based diagnostic assays was and perhaps still is well-founded. A study published this month (Chambers et al., 2015) showed that a single mutation in the H3N2 influenza virus was responsible for the antigen drift that caused the relative inefficacy of the flu shot in 2014 (around 50% effective), which sickened as many as 1,700 in the US (Subtype A).


Carroll MW et al. 2015. Temporal and spatial analysis of the 2014–2015 Ebola virus outbreak in West AfricaNature, doi:10.1038/nature14594.

Chambers BS et al. 2015. Identification of Hemagglutinin Residues Responsible for H3N2 Antigenic Drift during the 2014-2015 Influenza Season. Cell Rep.  pii: S2211-1247(15)00588-4. doi: 10.1016/j.celrep.2015.06.005.

Gire, S.K., et al. 2014. Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak.Science 345, 1369–1372.

Olabode AS et al., 2015. Ebolavirus is evolving but not changing: No evidence for functional change in EBOV from 1976 to the 2014 outbreak. Virology. 482:202-7. doi: 10.1016/j.virol.2015.03.029.

Park et al., DJ  2015. Ebola virus epidemiology, transmission, and evolution during seven months in Sierra Leone. Cell doi:10.1016/j.cell.2015.06.007.

Faith, Fear, Reason, Science, Belief, Dogma and Infectious Disease Policies on Ebola

Faith, Fear, Reason, Science, Belief, Dogma and Infectious Disease Policies on Ebola

EVERY DAY we learn that the situation in Sierra Leone is worsening.  It’s been six weeks of large numbers of new cases of Ebola.  People who had hoped that we could see elimination are slowly realizing the sober truth: there risk of Ebola becoming endemic in the population is increasing.  This outcome would be provide a constant risk of a pandemic.

Evolution_is_RealAs an evolutionary biologist in a religious world, I recognize now, much the same as did eight months ago, an urgent need to share objective information about Ebolavirus.  I am an ardent defender of science as a way of knowing, but I do not participate in the anti-religious fervor that grips some of the atheist/agnostic community in the US.  In my view, one cannot say that they hold dear the Constitution of the United States, and eschew freedoms of religion at the same time.  The value of the expression of one’s faith is not only guaranteed by the Constitution, but it is held sacrosanct.

Reconciling science and religion is likely to be found a fruitless endeavor; as any good scientist knows, there is a demarcation between the knowable and unknowable; the testable and untestable.  Religion makes knowledge claims that cannot, and will not, ever be tested by science.  On such issues not only should science be mute; the formal logic of science provides zero information on whether any particular deity might exist.

As I studied the Ebola crisis, in performing research and interviews for the book, I became a student of humanity.  The total sum of the history of how the outbreak became an epidemic involved a convoluted and contorted mess of logic knots and inputs from every walk of life.  At the center of the epidemic, time and time again, I found humanity struggling to fill the void of ignorance about Ebola with a balance of science, reason, evidence, and rational thinking.  I also saw humanity attempt to fill in the blanks with fear, belief, emotion, and faith.   Somehow, these factors all had to interact to make public policy, and to cause people to act in specific ways consistent with shutting down the spread of the disease.  I saw people at both ends of the cognitive spectrum act in ways that reveal their dogmatic positions.  I found strength and compassion at both ends, and I found people using guilt, shame, post-hoc rationalization, and dogma to justify their position.

Public health policy decisions must be made in real time.  We have not been able to sorted out the differences among the various forms of religion over the last 4,000 years.  Policy decisions cannot wait: at times when data are lacking, they must be made with incomplete evidence.    I found that some of the public health policy statements and positions made thus far in the Ebola crisis lacking in terms of logical rigor.  I also found, time and again, zealots willing to look past the fact, for the sake of pushing a particular agenda, seemingly at all costs, sacrificing reason and science for the sake of influence over policy.  Scientists have their dogma, too.

Time and againthat I witnessed dangerous ignorance amplified by incorrect public statements about the nature of the virus, I found religious dogma at the center, making things worse.  People in some countries in Western Africa are not even taught the germ theory of disease: they had to be educated by the “Ebola is Real” campaign.  Many times, their minds would fill in the blanks where existing knowledge could be very helpful to their own survival – and helpful in shutting down the spread of Ebola – with superstitious beliefs and theories of curses and witchcraft.  Today, we learned that the WHO may have delayed putting out the call for emergency help with the outbreak out of concern over appearing hostile to Muslims wishing to make their pilgrimage to Mecca in October.

At the height of the epidemic,  government officials in the US dogmatically chastised the press for asking about the likely of Ebola being “airborne”.  The book explores the issue of this question in some detail.  The logic of the statements that “there is no evidence that Ebola is airborne”, and whether it is good idea to rely on the absence of evidence, is given a thorough treatment. While “airborne” may be a misnomer, it’s really a matter of size.  The American Academy of Pediatrics recommends against the use of baby powder because of the risk of respiratory problems.  Talcum powder, at ten microns, is known to cause respiratory distress in some babies.  Ebola, at 970 nm (0.97 microns)  is ten times smaller.  One-hundred thirteen ebola viruses could be lined up, end to end, on the thin edge of a dollar bill.  Ebola is smaller than most other infectious agents that are known to be airborne; in fact, it is smaller that the flu virus.  Some have chastised others for daring the ask questions about transmission modes, and have resorted to ad hominum attacks.  It is never irrational to ask objective questions in science; it is, however, irrational to draw scientific conclusions in the absences of evidence.

Eventually, enough people died to convince most in Western Africa that Ebola is Real.  There are hold-outs; they certainly fear the truth.  But the stench of the decaying bodies and the thousands of orphans are no substitute for scientific knowledge.  Informing populations in areas where education is rare and ignorance reigns supreme, when done in a reactionary manner, is ineffective.  Ironically, progress could not be made in Guinea until the Imams were given the task of educating the faithful. The Imams’ positions in society as leaders, not their specific roles, made them key players in helping to bend the curve.  Sociologists and cultural anthropologists could have told us that.

I use the book to advocate for education about biological health risks between outbreaks.  And, I will practice what I preach. Along with copies of my book, I will be sending used textbooks on Evolution and related topics to three public libraries: one in Sierra Leone, Guinea, and one in Liberia.

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