Nanotechnology to study mutation in viruses

Sutrisha Kundu

3rd April, 2021

Microscopic view of the coronavirus.jpg

A mutation is a change in the sequence of bases of DNA or RNA in a genome. Some mutations occur during the replication of the genome due to errors made by the polymerase enzymes that replicate DNA or RNA. Unlike DNA polymerase, RNA polymerase is more prone to errors as it is not capable of “proofreading” its work. Viruses with RNA-based genomes, like HIV, coronavirus, therefore accrue mutations faster than viruses with DNA-based genomes. Also, single-stranded viruses mutate faster than double-stranded viruses. It has been found that the genome size appears to correlate negatively with the mutation rate. The mutation rate of an organism is thus defined as the probability that a change in genetic information is passed to the next generation. The capacity of some viruses to adapt to new hosts and environments is highly dependent on their ability to generate de novo diversity within a short period of time. Therefore, RNA viruses develop resistance to antiviral drugs very fast.

An influenza virus binds to the respirat

An influenza virus binds to the respiratory tract cell to enter and infect the cell

In viruses, one generation is often defined as a cell infection cycle, which includes an attachment to the cell surface, entry into the cell, gene expression, replication, encapsidation (enclosure of the viral nucleic acid within a capsid structure), and release of infectious particles. Mutations are not only restricted to replication but also occur due to spontaneous nucleotide changes. Viral mutation rates are influenced by multiple factors such as polymerase fidelity, template secondary structure, cellular microenvironment, replication mechanisms, proofreading, post–replication repair machinery, host enzymes, and some special genetic elements located within certain viral genomes whose function is to produce new mutations. Furthermore, mutation rates can also be regulated by altering the proteins involved in replication (except polymerase), mode of replication, and template structure and sequence.

Over the past decades, advancements in transistor technologies have resulted in much smaller and faster digital technologies. However, these critical circuit components are gradually becoming smaller and smaller in structure to occupy the least volume, initiating a global effort to find a new type of technology that can supplement, if not replace, the transistors.

Apart from this "scaling-down" problem, transistor-based
digital technologies face some other challenges as well. For example:

  1. they struggle at finding optimal solutions when presented with large sets of data. 

  2. Another arduous task for digital machines is pattern recognition, such as identifying a face as the same regardless of viewpoint or recognizing a familiar voice buried within a din of sounds.

 

But tasks that can send digital machines into a computational tizzy are ones at which the brain excels. The brain is not just quick at recognition and optimization problems, but also consumes far less energy than digital systems. Scientists think that neuromorphic systems will be able to overcome some of the computational hurdles faced by digital technology by mimicking how the brain solves these types of tasks.

Simultaneously, nanotechnology has been gaining importance as an emergent field  . . It mainly focuses on the atomic, molecular, and supramolecular levels. The ameliorations in nanotechnology have enabled a drastic decrement in the size of wireless devices, which has eventually resulted in the rise of a unique class called nanodevices. 

Nanodevices are nanoparticles created for interacting with cells and tissues and carrying out various specific tasks. The most famous nanodevices are imaging tools. Oral pills containing miniature cameras are swallowed which then reach the deep parts of the body and provide high-resolution pictures of cells as small as 1 μm in width. This makes them very useful for diagnostic purposes. 

Nanotechnology is also used to design nanorobots, based on the principles of bio-mimicry and pseudo-intelligence. In DNA biosensors, the sensitive component is normally composed of one ssDNA that facilitates the hybridization of complementary single-stranded molecules. These devices can sense and respond to various stimuli or signals. All the DNA-based sensing devices possess the ability to sense ions, proteins, nucleic acid sequences, pH, and glucose levels. DNA can be integrated within a transducer through immobilization by covalent interaction, crosslinking, or adsorption. DNA is labeled with chemiluminescent probes, ligands, or radioactive probes such as biotin because normal DNA does not provide any signal by itself. 

 

Combining these two technologies, a brain cell-like nanodevice has been created to study mutations in viruses. This nanodevice consists of layers of different inorganic materials, each with a unique function. The most important of these inorganic materials is the compound niobium dioxide.

An electron micrograph of the artificial

An electron micrograph of the artificial neuron. The niobium dioxide layer (yellow) endows the device with neuron-like behavior

When voltage is applied to this region of niobium dioxide, its temperature begins to increase. As the temperature reaches a critical value, niobium dioxide changes from an insulator to a conductor. However, as soon as it begins to conduct electricity, its temperature drops and it switches back to the insulator state. This continuous transition enables the nanodevice to generate a pulse of electrical current that closely resembles the pattern of action potentials, occurring in biological neurons. Also, by varying the voltage across the synthetic neurons, various neuronal behaviors as observed in the brain can be obtained, such as sustained, burst, and chaotic firing of electrical spikes. To date, 15 such types of neuronal firing profiles have been successfully recreated, all using a single electrical component and at much lower energies compared to transistor-based circuits.

Further research has been conducted to check how these synthetic neurons can solve real-world problems. Initially, 24 nanoscale devices were wired together into a network similar to the connections between the brain's cortex and thalamus. Next, this system was employed to solve a toy version of the viral quasispecies reconstruction problem, to identify viral mutations without a reference genome.

The network was then converted to short gene fragments by programming the strength of connections between the synthetic neurons of the network. Within a few microseconds, the network of artificial neurons settled down. This result served as proof that these artificially developed neuromorphic systems could quickly perform tasks in an energy-efficient way.

Thus, it was successfully demonstrated by the researchers that their brain-inspired system could identify possible mutations in a virus. This identification is very useful for ensuring the efficacy of vaccines and medications against viral strains exhibiting genetic diversity. Considering the current situation and pacing of further research in the field these brain-like synthetic networks will eventually be able to solve more complex problems and be commercially viable.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

References:

  1. M. Mehta, K. Subramani. Nanodiagnostics in Microbiology and Dentistry,  Emerging Nanotechnologies in Dentistry. 2012; 365 – 390. Published 2012.                                                                                https://doi.org/10.1016/B978-1-4557-7862-1.00021-3.

  2. Manuela Tatiana, Nistor Alina, Gabriela, Rusu. Nanorobots With Applications in Medicine, Polymeric Nanomaterials in Nanotherapeutics. Published 2019; 123 – 149.                                      https://doi.org/10.1016/B978-0-12-813932-5.00003-0.

  3. Williams R.S. , Kumar. New brain cell-like nanodevices work together to identify mutations in viruses, Texas A & M University. Published 2020 Sep 24. https://www.sciencedaily.com/releases/2020/09/200924082722.htm#:~:text=Summary%3A,in%20a%20brain%2Dlike%20manner.

  4. Rafael Sanjuán and Pilar Domingo-Calap. Mechanisms of viral mutation, Nature Public Health Emergency collection. 2016; 73(23): 4433–4448. Published 2016 Jul 8. doi: 10.1007/s00018-016-2299-6.

  5. JoVE Core Biology. Chapter 16: Viruses; 16.7: Viral Mutations.                                https://www.jove.com/science-education/10827/viral-mutations

Construction of artificial neuron to det

About the Author

20200818_180848 - Sutrisha Kundu.jpg

Sutrisha is from St. Xavier's College, Kolkata, currently studying in 3rd year of Integrated MSc in Biotechnology. She is engaged in an ongoing project titled "Rice Hisk Ash."