about

about us

NIH

According to the National Institute of Allergy and Infectious Diseases,

in the past 20 years, out of the newly recognized pathogens that impact human and animal health, approximately 44 percent are viruses. Many of these pathogens, particularly RNA viruses, are characterized by a high mutation rate that allows them to rapidly adapt to a changing environment, as occurred in the 2009 H1N1 pandemic. Additionally, many viruses undergo more widespread genetic events (e.g., rearrangements, reassortments) that significantly alter the viral genome. These changes can produce virions capable of evading existing vaccine-acquired and convalescent immunity in humans and animals.

The goal of DARPA’s Prophecy (Pathogen Defeat) program

The goal of DARPA’s Prophecy (Pathogen Defeat) program [1. Hepburn, Matthew, MD,”Prophecy (Pathogen Defeat).” Prophecy (Pathogen Defeat). DARPA, n.d. Web. 16 Feb. 2016.] was to organize a team of researchers that could identify how genetic events significantly alter the viral genome. Researchers from Stanford University UCSF and the Tauber Bioinformatics Research Center at Haifa University teamed up to develop a biological method (CirSeq) and computational approaches to investigate viral adaptation and interaction with hosts(host-pathogen circuitry). As a result, the funding from DARPA helped create the CirSeq method published in Nature in 2014 [2. Acevedo, Ashley, Leonid Brodsky, and Raul Andino. “Mutational and Fitness Landscapes of an RNA Virus Revealed through Population Sequencing.” Nature 505.7485 (2013): 686-90. Web. 16 Feb. 2016.] and the T-BioInfo platform’s section dedicated to Virology.

virology

One of the outcomes of the DARPA project was a close collaboration between researchers studying viral SNVs, host data, including protein-protein interactions and post translational modifications of proteins. The computational challenges in analyzing this heterogenous datasets helped define the concept of “fitness” that can characterize a viral mutation into beneficial, neutral, detrimental and lethal. These characteristics can be mapped onto the protein surfaces to show which areas are highly conserved and are better targets for intervention, enabling a more precise way for vaccine and drug development.

The DARPA project was addressing an important challenge posed by Dengue, a virus that is characterized by similar symptoms, even though 4 defined virus types have been studied.[3. “Dengue Viruses.” Nature.com. Nature Publishing Group, n.d. Web. 15 Feb. 2016.]

interactive-demo

View an interactive demo

The DARPA project was addressing an important challenge posed by Dengue, a virus that is characterized by similar symptoms, even though 4 defined virus types have been studied.[3. “Dengue Viruses.” Nature.com. Nature Publishing Group, n.d. Web. 15 Feb. 2016.]

rgb

Ebola, Chikungunya, Dengue and Zika have all been recently in the news because of how these virus strains can become pandemic and resistant to known treatments. What can be evident about these pandemic strains is their ability to cross over from vectors such as mosquitos to humans or between organs (such as Polio). In addition, Many viruses, in particular RNA viruses, have short generation times and relatively high mutation rates (on the order of one point mutation or more per genome per round of replication for RNA viruses).

This elevated mutation rate, when combined with natural selection, allows viruses to quickly adapt to changes in their host environment. The rapidity of viral mutation also causes problems in the development of successful vaccines and antiviral drugs, as resistant mutations often appear within weeks or months after the beginning of the treatment. One of the main theoretical models to study viral evolution is the quasispecies model, as the viral quasispecies.[4. Viral evolution. (2016, February 2). In Wikipedia, The Free Encyclopedia. Retrieved 19:50, February 16, 2016, from https://en.wikipedia.org/w/index.php?title=Viral_evolution&oldid=702928609

This elevated mutation rate, when combined with natural selection, allows viruses to quickly adapt to changes in their host environment. The rapidity of viral mutation also causes problems in the development of successful vaccines and antiviral drugs, as resistant mutations often appear within weeks or months after the beginning of the treatment. One of the main theoretical models to study viral evolution is the quasispecies model, as the viral quasispecies.[4. Viral evolution. (2016, February 2). In Wikipedia, The Free Encyclopedia. Retrieved 19:50, February 16, 2016, from https://en.wikipedia.org/w/index.php?title=Viral_evolution&oldid=702928609]

The difficulty in developing a vaccine for RNA viruses lies in their ability to change fast and evolve in a way to avoid the host immune system and evade current drug methods. These RNA virus populations often don’t have a single member but are instead made up of many non-identical, but related members called quasispecies.

Currently, there are no reliable systems to predict viral reassortment or mutations responsible for the emergence of new viral strains. Traditional sequencing methods do not have the resolution needed to identify the mutations that create these subpopulations and thus cannot differentiate the mutations responsible for virus survival and adaptation.

Pine Biotech utilized both a biological and computational method to accurately measure the viral mutations of various RNA viruses, including Poliovirus. The biological method, known as CirSeq[1. Acevedo, Ashley, Leonid Brodsky, and Raul Andino. “Mutational and Fitness Landscapes of an RNA Virus Revealed through Population Sequencing.” Nature 505.7485 (2013): 686-90. Web. 16 Feb. 2016.], improves traditional sequencing methods. CirSeq allows for the identification of individual viral strains with highly accurate sequence data within a population.

The utilization of this innovated sequencing platform allows for a new genetic approach to study the evolution of viruses and their host. CirSeq works by converting viral RNA’s to circular molecules, thus the mutations by the virus remain with the repeats, but errors are not produced during copying.

When CirSeq was applied to the Poliovirus, researchers were able to define single nucleotide mutation rates and established a platform for studying underlying evolution of virus populations in human cells. Additionally this method can be used to determine the fitness of each base at every position in the genome, allowing analysis determination of which bases are neutral and which are selected and can provide new innovative techniques.

Genome-wide fitness calculations enabled by CirSeq, combined with structural information, can provide high-definition, bias-free insights into structure-function relationships, potentially revealing novel functions for viral proteins and RNA structures, as well as nuanced insights into a viral genome’s phenotypic space. Such analyses have the power to reveal protein residues or domains that directly correspond to viral functional plasticity and may significantly inform our structural and mechanistic understanding of host–pathogen interactions.

For more information on the T-BioInfo platform

view site

bioinfo-platrform

Partnering Companies

partner-2

Raul Andino

Raul Andino, Ph.D., is professor of microbiology and immunology at the University of California, San Francisco, where he specializes in RNA viruses, with a focus on molecular biology, immunology, and evolutionary biology. His research includes mechanisms of replication, antiviral RNAi, and adaptation. He has served on several national advisory panels and is currently chair of the NIAID Systems Biology Program (SysBio) Steering Committee. (October 31, 2019)

partner-1

Leonid Brodsky

Dr. Leonid Brodsky has been leading bioinformatics research, computational biology and systems biology modeling for over 30 years. Currently, he is the director of Tauber Bioinformatics Research Center at the University of Haifa. His expertise and methods contributed to numerous scientific publications. Many of the data analysis, mathematical modeling and machine learning methods are a part of the T-BioInfo bioinformatics platform.

Participating institutions