For many scientists around the world, 2020 has been a challenging year. Nevertheless, let’s take a moment to appreciate many exciting technologies and inspiring breakthroughs in the fields of molecular biology and biochemistry from this unforgettable year.
New Approach to Vaccines
According to Centers for Disease Control and Prevention or CDC, some of the first vaccines for COVID, authorized for use in the United States, are messenger RNA vaccines or mRNA vaccines.
These types of vaccines were never developed and authorized in the United States until this year, although mRNA vaccines have been studied for other diseases, such as flu, Zika, rabies, and cytomegalovirus.
What makes mRNA vaccines extraordinary: these vaccines use mRNA molecules to activate the immune cells, instead of using a weakened virus that causes the disease. mRNA is a single-stranded molecule, carrying the genetic information to make a protein.
After entering the immune cells, mRNA molecules in the vaccine provide instructions to produce a harmless protein, such as a spike protein. The cells then recognize that this protein is not a part of the cells and start making antibodies. This process is similar to what happens naturally when our body fights an infection against many pathogens, including viruses.
This breakthrough may pave a way to develop more mRNA vaccines for many other infectious diseases and deliver a single shot of multiple vaccines.
To learn more about how mRNA vaccines work, watch the video below:
Predicting Protein Structure
Predicting the structure of a protein helps scientists learn about its identity and possible function. As an example, it could provide information about whether a particular protein can cause a disease or cure a disease.
Common techniques to study protein structures are nuclear magnetic resonance, X-ray crystallography, and cryo-electron microscopy, but they can be difficult, costly, and time-consuming.
Another method to study protein structures is by using artificial intelligence or AI. This approach is much easier than X-ray crystallography. However, early attempts in the 1980s and 1990s performed poorly, and scientists from different labs were unable to replicate these methods.
In November 2020, a London-based AI lab, DeepMind Technologies, used a program, called AlphaFold, which demonstrated an impressive accuracy level in a biennial protein-structure prediction challenge, Critical Assessment of Structure Prediction or CASP.
What makes this tool outstanding is AlphaFold soon could help scientists to illuminate the functions of numerous unsolved proteins in the genomic database.
To learn more about the behind-the-scenes story of this technology, watch the video below:
Viewing Tiny Nanoparticles
For researchers, using current techniques to produce images from miniscule particles, such as nanoparticles or viruses, can be very challenging.
Small and transparent particles are unable to reflect or absorb enough light, so they remain invisible under a standard imaging bright-field microscope. This type of microscopes usually detects a transparent object between 100—200 nm.
To make a transparent particle visible, researchers need to use labeling method. However, before performing this method, they must know and understand a particular feature of the particle in order find a way to label that feature with fluorescent dye.
To overcome these drawbacks, scientists built a new optical imaging technology called PANORAMA to visualize tiny particles without labeling (Ohannesian et al., 2020). This technology uses a glass slide covered with gold nanodiscs, so researchers can monitor changes in the transmission of light and detect small particles.
By using this technology and a standard imaging bright-field microscope, scientists reported they could detect nanoparticles as small as 25 nanometers, which is the size of the smallest polystyrene particle on the market. To put it in perspective, influenza virus particles have a size of 80—120 nm.
Finding Potential Therapeutic Targets throughout Genome
Conventional sequencing and screening tools have limitations on discovering DNA methylation sites in the whole genome. As an example, the conventional methods can only screen certain lengths at a time, but not throughout the genome.
Finding these DNA methylated sites are helpful for developing new therapeutic agents or targeted therapy.
DNA methylation is a process involving the addition of methyl groups into the DNA molecule at particular sites. DNA methylation plays a major role in many cellular processes, such as cell differentiation and embryonic development. The methylation of DNA prevents the binding of transcription factors to DNA, causing the repression of a gene.
Any mistakes in the DNA methylation process can lead into the development of many diseases, such as cancers in colon breast, liver, and bladder.
To predict DNA methylation sites in the genome, researchers developed a new software, Deep6mA, by using an algorithm through machine learning (Tan et al., 2020). This algorithm can find disease-causing mechanisms, usually missed by conventional screening methods. This study was done by using supercomputers supported by the U.S. National Science Foundation.
Exploring Less Understood Genomes or Genes
In recent years, portable sequencing devices have become available for researchers to use outside the labs. A software to quickly read and analyze sequencing results during sample collection, paired with the devices, could certainly improve the quality and quantity of data.
To allow genetic tests or diagnoses on the experimental sites, scientists developed a software, called UNCALLED (Kovaka et al., 2020).
This software identifies DNA molecules inside electrified holes in the sequencer and reads the data within less than a second. It then checks, compares the data with a specified genome’s reference sequence, and maps the desired molecules. The software can also reverse the voltage in the electrified hole to reject undesired molecules, so the next molecule can come in.
The scientists who developed this software demonstrated two possible applications of this software. For the first application, it selectively sequenced molecules from a certain species from the field and rejected molecules from a well-studied microbe, such as Escherichia coli. This method could be useful for scientists interested in exploring the genome of a less-understood microbe.
They also showed another application of this software: enhancing the sequencing of 148 cancer-related genes and profiling their variants with a single run. It made it possible to capture in real-time many complex mutations in the cancer genes, easily missed from performing a standard sequencing.
A New Smartphone App to Analyze DNA Sequencing
Scientists have also developed a smartphone app, iGenomics, to be used with a portable sequencer to create a mobile genetics laboratory (Palatnick et al., 2020). This app could potentially replace the use of computers to analyze sequence data.
This smartphone app is potentially useful to analyze DNA data in remote locations without any internet availability. In addition, researchers could also send data to another researcher in a different location.
The scientists and an engineer, who developed iGenomics, reported the app quickly identified DNA sequences of viruses, such as influenza virus and Zika virus, and recognized mutations; both could be helpful for diagnosis and treatment.
Find out more about iGenomics from the video below:
PCR-Like Lab-on-a-Chip
When dealing with infectious pathogens, rapid screening and low-cost diagnosis are the two important steps. However, to target the nucleic acids for pathogen detections, a tabletop PCR machine in the labs is the most common technology for researchers.
The development of portable PCR equipment, which is small and easy to use, may soon change the future of diagnostic tools for diseases.
A typical diagnostic process requires a significant amount of time: from bringing the samples from the field to the lab, extracting the nucleic acids, running the PCRs, analyzing the data, and reporting the result. Without a doubt, portable equipment, which does all of these steps rapidly, could reduce the spread of diseases.
One of the many lab-on-a chip technologies built in 2020 for disease diagnostic tools is TriSilix. A team of researchers developed this tiny PCR chip made of silicone, powered by a typical smartphone battery (Nunez-Bajo et al., 2020). The researchers thought that the simplicity of this technology might help the production of the chips to be relatively cheap and easy.
To find out more about this technology, watch the video below:
The chip, designed for a patient to use at home, has a DNA sensor, a heater, and a sensitive temperature gauge. Like a tool providing results for a blood sugar test at home, the lab-on-a-chip may also provide quick results for infectious diseases. In the future, researchers hoped that this device could help people test themselves at home for diseases, such as cold and the flu.
References
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