By Sotirios (Sam) Kolontouros
The Problem of Emerging
Since the middle of the 20th century both urbanization and agricultural activity has been encroaching on remote wilderness that has had little contact with large human populations. Although this is not unique in history, it has been occurring at both an unprecedented pace and a global scale. The areas that have been impacted the most are among the most rich and diverse incubators of a variety of viruses that the human species has had little contact with. This has led to emerging zoonotic diseases that crash into the population in outbreaks, epidemic and pandemics. With a world interconnected through modern aviation, no point on the planet is more than a day away from direct contact with any other point. This has magnified the problem astoundingly.
Some of these are deadly viruses, such as H5N1 and Ebola with a mortality rate that is high enough to pose an existential threat to civilization. Others are less deadly but can be no less disruptive and become a serious global public health problem. The challenge we face is how to quickly identify these emerging threats early enough to prevent them from breaking out of their natural habitats and zoonotic reservoirs and into the global human population. What is needed is a bio-surveillance network and capabilities that can operate at speed suited to a world where an infected passenger can climb aboard a jet in Hong Kong, catch a connecting flight in San Francisco, attend a business meeting in Chicago and arrive in New York in less than 48 hours. Identifying tools and intelligence streams that can be used to build such a network is critical to this goal.
Replikin Count and Viral
One of the most useful tools for bio-surveillance of emerging diseases would be a way to survey emerging and existing zoonotic viruses and determine their pandemic potential well before they crash into the human population. Simulating evolution in the laboratory or on a computer can only be done with models that are highly simplified versions of nature. Thus looking at specific mutations and determining what evolutionary effect that mutation might have is challenging and not guaranteed to lead to useable results.
Some mutations’ effects are easy to link to a possible future outbreak. An example would be a mutation that is shown to make the virus more virulent. Another would be one that changes its mode of transmission. Until recently there hasn’t been a genetic mutation or mechanism that is both universal across virtually all RNA viruses and who’s dynamics lead to a consistent and repeatable way to predict an outbreak. In 2011 a new class of polypeptides called Replikins were discovered that are embedded within viral genes (Bogoch & Bogoch, Nature Proceedings, 22 Aug 2011).
These polypeptides form a scaffolding whose structure is unique to each virus and shared across generations. What is most remarkable is that these polypeptides are associated with increased viral replication, virulence and infection rates. The exact mechanism that causes this is not well understood, however their concentration in viruses have been observed to change over time and sudden, statistically significant increases in their concentration is strongly correlated to viral outbreaks and epidemics.
The concentration of Replikin is measured within an infected cell, such as hemagglutinin, which is an infected human red blood cell. The bioinformatics processes used, GeneForecast™ and FluForecast™ , are proprietary and patented by Replikin Ltd. In the graphic below we see how increased Replikin count correlates with a larger surface area for the peptide chain scaffolding on the surface of H1N1 hemagglutinin.
We can also see how spikes in shared Replikin count in Influenza viruses have a strong correlation to outbreaks as far back as the 1918 Spanish Influenza.
The correlation to viral outbreaks is not isolated to Influenza. It has been observed in Dengue Fever, Chikungunya, SARS, Malaria, Mers-CoV and Foot & Mouth Disease. Through the examination of historical samples of Influenza and other viruses it has been shown that replikin spikes occur between 18 months to two years in advance of outbreaks of the virus. Most dramatically it was seen in Ebola two years before the 2014 outbreak in West Africa. Statistically significant Replikin count was also measured in Zika in September of last year right before the current outbreak went exponential.
As of July 2015 over three million gene sequences in the Pubmed catalog were analyzed. A total of 41 outbreaks by 16 virus strains were all preceded by statistically significant spikes in replikin count.
Recently genetic samples were taken of Zika to explore the replikin concentrations since its discovery in 1947. Measured in number of peptides per 100 amino acids, shared Zika replikins were observed to have never gone over 9.6 and to have remained at an average of 4.0 until the Easter Island outbreak in 2007. Samples from Brazil in 2016 have counts as high as 13.6 with 75% of samples greater than 4.0. Although this did not fall within the average lead time, samples of Zika were not taken until the disease had reached the Western Hemisphere. Had surveillance of Zika replikin count occurred on regular intervals the current outbreak time frame would have been predicted with remarkable accuracy.
Furthermore, while spikes in replikin count are precursors to an outbreak, a significant and sudden drop in replikin count was observed in October 2015 and correlates to the burnout of the Ebola outbreak in West Africa by early spring in 2016.
Replikin count changes over time is not the only factor to take into account when determining the probability, severity or longevity of an outbreak. In the case of Ebola there was also a marked decrease in its lethality, which made an outbreak more likely to occur and last over a longer period of time. In the case of Zika this is not a consideration as its lethality is negligible. In both cases replikin count is a valuable tool in the surveillance of the virus.